Team research paper and project on network vulnerabilities with multiple attacks and defesnses:
Cybersecurity
-For this project, our class was paired with teams to attempt to find vulnerabilities in other teams networks and to successfully beach their network.
-My role in this group was to help breach other team vulnerabilities through different attacks like responder attacks, honeypots, etc.
-The main challenges of this project were trying to find the vulnerabilities successfully, as the whole team had troubles with each of our different attacks and defenses.
-We learned how to use cybersecurity tools to help find vulnerabilities in networks and how to protect against them better. For example, in the honeypot we used we deployed it to port 80, when the attacker tried to access our fake server we were notified. We also deployed palto alto firewall to create our private and secure network. For an attack, we also used password crackers like john the ripper. This project taught us how to breach networks as a team.
Network Forensics is scientifically proven technique to accumulate, perceive, identify, examine, associate, analyse and document digital evidence from multiple systems for the purpose of uncovering the fact of attacks and other problem incident as well as performing the action to recover from the attack. Many systems are proposed for designing the network forensic systems. In this paper we have prepared comparative analysis of various models based on different techniques.
Network security using data mining conceptsJaideep Ghosh
Network Security is a major part of a network that needs to be maintained because information is being passed between computers etc. and is very vulnerable to attack.
Data Mining is the process of extraction of required/specific information from data in database.
Data mining is integrated with network security and can be used with various security tools as well as hacking tool.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Classification Mechanism For Network Intrusion Detection System Based on Data Mining
Techniques:A Survey..........................................................................................................................1
Subaira A. S. and Anitha P.
Automated Biometric Verification: A Survey on Multimodal Biometrics ..............................................1
Rupali L. Telgad, Almas M. N. Siddiqui and Dr. Prapti D. Deshmukh
Design and Implementation of Intelligence Car Parking Systems ........................................................1
Ogunlere Samson, Maitanmi Olusola and Gregory Onwodi
Intrusion Detection Techniques for Mobile Ad Hoc and Wireless Sensor Networks..............................1
Rakesh Sharma, V. A. Athavale and Pinki Sharma
Performance Evaluation of Sentiment Mining Classifiers on Balanced and Imbalanced Dataset ...........1
G.Vinodhini and R M. Chandrasekaran
Demosaicing and Super-resolution for Color Filter Array via Residual Image Reconstruction and Sparse
Representation..................................................................................................................................1
Jie Yin, Guangling Sun and Xiaofei Zhou
Determining Weight of Known Evaluation Criteria in the Field of Mehr Housing using ANP Approach ..1
Saeed Safari, Mohammad Shojaee, Mohammad Tavakolian and Majid Assarian
Application of the Collaboration Facets of the Reference Model in Design Science Paradigm ...............1
Lukasz Ostrowski and Markus Helfert
Personalizing Education News Articles Using Interest Term and Category Based Recommender
Approaches .......................................................................................................................................1
Network Forensics is scientifically proven technique to accumulate, perceive, identify, examine, associate, analyse and document digital evidence from multiple systems for the purpose of uncovering the fact of attacks and other problem incident as well as performing the action to recover from the attack. Many systems are proposed for designing the network forensic systems. In this paper we have prepared comparative analysis of various models based on different techniques.
Network security using data mining conceptsJaideep Ghosh
Network Security is a major part of a network that needs to be maintained because information is being passed between computers etc. and is very vulnerable to attack.
Data Mining is the process of extraction of required/specific information from data in database.
Data mining is integrated with network security and can be used with various security tools as well as hacking tool.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Classification Mechanism For Network Intrusion Detection System Based on Data Mining
Techniques:A Survey..........................................................................................................................1
Subaira A. S. and Anitha P.
Automated Biometric Verification: A Survey on Multimodal Biometrics ..............................................1
Rupali L. Telgad, Almas M. N. Siddiqui and Dr. Prapti D. Deshmukh
Design and Implementation of Intelligence Car Parking Systems ........................................................1
Ogunlere Samson, Maitanmi Olusola and Gregory Onwodi
Intrusion Detection Techniques for Mobile Ad Hoc and Wireless Sensor Networks..............................1
Rakesh Sharma, V. A. Athavale and Pinki Sharma
Performance Evaluation of Sentiment Mining Classifiers on Balanced and Imbalanced Dataset ...........1
G.Vinodhini and R M. Chandrasekaran
Demosaicing and Super-resolution for Color Filter Array via Residual Image Reconstruction and Sparse
Representation..................................................................................................................................1
Jie Yin, Guangling Sun and Xiaofei Zhou
Determining Weight of Known Evaluation Criteria in the Field of Mehr Housing using ANP Approach ..1
Saeed Safari, Mohammad Shojaee, Mohammad Tavakolian and Majid Assarian
Application of the Collaboration Facets of the Reference Model in Design Science Paradigm ...............1
Lukasz Ostrowski and Markus Helfert
Personalizing Education News Articles Using Interest Term and Category Based Recommender
Approaches .......................................................................................................................................1
Network Forensic Investigation of HTTPS ProtocolIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Survey on classification techniques for intrusion detectioncsandit
Intrusion detection is the most essential component
in network security. Traditional Intrusion
Detection methods are based on extensive knowledge
of signatures of known attacks. Signature-
based methods require manual encoding of attacks by
human experts. Data mining is one of the
techniques applied to Intrusion Detection that prov
ides higher automation capabilities than
signature-based methods. Data mining techniques suc
h as classification, clustering and
association rules are used in intrusion detection.
In this paper, we present an overview of
intrusion detection, KDD Cup 1999 dataset and detai
led analysis of different classification
techniques namely Support vector Machine, Decision
tree, Naïve Bayes and Neural Networks
used in intrusion detection.
The Next Generation Cognitive Security Operations Center: Network Flow Forens...Konstantinos Demertzis
A Security Operations Center (SOC) can be defined as an organized and highly skilled team that uses advanced computer forensics tools to prevent, detect and respond to cybersecurity incidents of an organization. The fundamental aspects of an effective SOC is related to the ability to examine and analyze the vast number of data flows and to correlate several other types of events from a cybersecurity perception. The supervision and categorization of network flow is an essential process not only for the scheduling, management, and regulation of the network’s services, but also for attacks identification and for the consequent forensics’ investigations. A serious potential disadvantage of the traditional software solutions used today for computer network monitoring, and specifically for the instances of effective categorization of the encrypted or obfuscated network flow, which enforces the rebuilding of messages packets in sophisticated underlying protocols, is the requirements of computational resources. In addition, an additional significant inability of these software packages is they create high false positive rates because they are deprived of accurate predicting mechanisms.
For all the reasons above, in most cases, the traditional software fails completely to recognize unidentified vulnerabilities and zero-day exploitations. This paper proposes a novel intelligence driven Network Flow Forensics Framework (NF3) which uses low utilization of computing power and resources, for the Next Generation Cognitive Computing SOC (NGC2SOC) that rely solely on advanced fully automated intelligence methods. It is an effective and accurate Ensemble Machine Learning forensics tool to Network Traffic Analysis, Demystification of Malware Traffic and Encrypted Traffic Identification.
An Intrusion Detection based on Data mining technique and its intended import...Editor IJMTER
Intrusion detection is a pivotal and essential requirement of today’s era. There are two
major side of Intrusion detection namely, Host based intrusion detection as well as network based
intrusion detection. In Host based intrusion detection system, it monitors the information arrive at the
particular machine or node. While in network based intrusion system, it monitor and analyze whole
traffic of network. Data mining introduce latest technology and methods to handle and categorize
types of attacks using different classification algorithm and matching the patterns of malicious
behavior. Due to the use of this data mining technology, developers extract and analyze the types of
attack in the network.
In addition to this there are two major approach of intrusion detection. First, anomaly based approach,
in which attacks are found with high false alarm rate. However, in signature based approach, false
alarm rate is low with lack of processing of novel attacks. Most of the researchers do their research
based on signature intrusion with the purpose to increase detection rate. Major advantage of this
system, IDS does not require biased assessment and able to identify massive pattern of attacks.
Moreover, capacity to handle large connection records of network. In this paper we try to discover
the features of intrusion detection based on data mining technique.
Survey on Host and Network Based Intrusion Detection SystemEswar Publications
With invent of new technologies and devices, Intrusion has become an area of concern because of security issues, in the ever growing area of cyber-attack. An intrusion detection system (IDS) is defined as a device or software application which monitors system or network activities for malicious activities or policy violations. It produces reports to a management station [1]. In this paper we are mainly focused on different IDS concepts based on Host and Network systems.
Cybercrime is increasing at a faster pace and sometimes causes billions of dollars of business- losses so
investigating attackers after commitment is of utmost importance and become one of the main concerns of
network managers. Network forensics as the process of Collecting, identifying, extracting and analyzing
data and systematically monitoring traffic of network is one of the main requirements in detection and
tracking of criminals. In this paper, we propose an architecture for network forensic system. Our proposed
architecture consists of five main components: collection and indexing, database management, analysis
component, SOC communication component and the database.
The main difference between our proposed architecture and other systems is in analysis component. This
component is composed of four parts: Analysis and investigation subsystem, Reporting subsystem, Alert
and visualization subsystem and the malware analysis subsystem. The most important differentiating
factors of the proposed system with existing systems are: clustering and ranking of malware, dynamic
analysis of malware, collecting and analysis of network flows and anomalous behavior analysis.
EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...IJNSA Journal
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
CYBER FORENSICS AND AUDITING
Topics Covered: Introduction to Cyber Forensics, Computer Equipment and associated storage, media Role of forensics Investigator, Forensics Investigation Process, Collecting Network based Evidence Writing, Computer Forensics Reports, Auditing, Plan an audit against a set of audit criteria, Information Security Management, System Management. Introduction to ISO 27001:2013
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
Detecting and Preventing Attacks Using Network Intrusion Detection SystemsCSCJournals
Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathway for intrusion. This system is designed to detect and combat some common attacks on network systems. It follows the signature based IDs methodology for ascertaining attacks. A signature based IDS will monitor packets on the network and compare them against a database of signatures or attributes from known malicious threats. It has been implemented in VC++. In this system the attack log displays the list of attacks to the administrator for evasive action. This system works as an alert device in the event of attacks directed towards an entire network.
Malicious activities (malcodes) are self replicating
malware and a major security threat in a network environment.
Timely detection and system alert flags are very essential to
prevent rapid malcodes spreading in the network. The difficulty
in detecting malcodes is that they evolve over time. Despite the fact
that signature-based tools, are generally used to secure systems,
signature-based malcode detectors neglect to recognize muddled
and beforehand concealed malcode executables. Automatic signature
generation systems has likewise been use to address the issue
of malcodes, yet there are many works required for good detection.
Base on the behavior way of malcodes, a behavior approach is
required for such detection. Specifically, we require a dynamic
investigation and behavior Rule Base system that distinguishes
malcodes without erroneously block legitimate traffic or increase
false alarms. This paper proposed and discussed the approach
using Machine learning and Indicators of Compromise (IOC) to
analyze intrusion in a network, to identify the cause of the attack
and to provide future detection. This paper proposed the use of
behaviour malware analysis framework to analyze intrusion data,
apply clustering algorithm on the analyzed data and generate IOC
from the clustered data for IOCRule, which will be implemented
into Snort Intrusion Detection System (IDS) for malicious code
detection.
A NOVEL HEADER MATCHING ALGORITHM FOR INTRUSION DETECTION SYSTEMSIJNSA Journal
The evolving necessity of the Internet increases the demand on the bandwidth. Therefore, this demand opens the doors for the hackers’ community to develop new methods and techniques to gain control over networking systems. Hence, the intrusion detection systems (IDS) are insufficient to prevent/detect unauthorized access the network. Network Intrusion Detection System (NIDS) is one example that still suffers from performance degradation due the increase of the link speed in today’s networks. In This paper we proposed a novel algorithm to detect the intruders, who’s trying to gain access to the network using the packets header parameters such as;
source/destination address, source/destination port, and protocol without the need to inspect each packet content looking for signatures/patterns. However, the “Packet Header Matching” algorithm enhances the overall speed of the matching process between the incoming packet headers against the rule set. We ran the proposed algorithm to proof the proposed concept in coping with the traffic arrival speeds and the various bandwidth demands. The achieved results were of significant enhancement of the overall performance in terms of detection speed.
Include at least 250 words in your posting and at least 250 words inmaribethy2y
Include at least 250 words in your posting and at least 250 words in your reply. Indicate at least one source or reference in your original post. Please see syllabus for details on submission requirements.
Module 1 Discussion Question
Search "scholar.google.com" for a company, school, or person that has been the target of a network
or system intrusion? What information was targeted? Was the attack successful? If so, what changes
were made to ensure that this vulnerability was controlled? If not, what mechanisms were in-place to protect against the intrusion.
Reply-1(Shravan)
Introduction:
Interruption location frameworks (IDSs) are programming or equipment frameworks that robotize the way toward observing the occasions happening in a PC framework or system, examining them for indications of security issues. As system assaults have expanded in number and seriousness in the course of recent years, interruption recognition frameworks have turned into an essential expansion to the security foundation of generally associations. This direction archive is planned as a preliminary in interruption recognition, created for the individuals who need to comprehend what security objectives interruption location components serve, how to choose and design interruption discovery frameworks for their particular framework and system situations, how to deal with the yield of interruption identification frameworks, and how to incorporate interruption recognition capacities with whatever remains of the authoritative security foundation. References to other data sources are likewise accommodated the peruse who requires particular or more point by point guidance on particular interruption identification issues.
In the most recent years there has been an expanding enthusiasm for the security of process control and SCADA frameworks. Moreover, ongoing PC assaults, for example, the Stunt worm, host appeared there are gatherings with the inspiration and assets to viably assault control frameworks.
While past work has proposed new security components for control frameworks, few of them have investigated new and in a general sense distinctive research issues for anchoring control frameworks when contrasted with anchoring conventional data innovation (IT) frameworks. Specifically, the complexity of new malware assaulting control frameworks - malware including zero-days assaults, rootkits made for control frameworks, and programming marked by confided in declaration specialists - has demonstrated that it is exceptionally hard to avert and identify these assaults dependent on IT framework data.
In this paper we demonstrate how, by joining information of the physical framework under control, we can distinguish PC assaults that change the conduct of the focused on control framework. By utilizing information of the physical framework we can center around the last goal of the assault, and not on the specific instruments of how vulnerabilities are misused, and how ...
Network Forensic Investigation of HTTPS ProtocolIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Survey on classification techniques for intrusion detectioncsandit
Intrusion detection is the most essential component
in network security. Traditional Intrusion
Detection methods are based on extensive knowledge
of signatures of known attacks. Signature-
based methods require manual encoding of attacks by
human experts. Data mining is one of the
techniques applied to Intrusion Detection that prov
ides higher automation capabilities than
signature-based methods. Data mining techniques suc
h as classification, clustering and
association rules are used in intrusion detection.
In this paper, we present an overview of
intrusion detection, KDD Cup 1999 dataset and detai
led analysis of different classification
techniques namely Support vector Machine, Decision
tree, Naïve Bayes and Neural Networks
used in intrusion detection.
The Next Generation Cognitive Security Operations Center: Network Flow Forens...Konstantinos Demertzis
A Security Operations Center (SOC) can be defined as an organized and highly skilled team that uses advanced computer forensics tools to prevent, detect and respond to cybersecurity incidents of an organization. The fundamental aspects of an effective SOC is related to the ability to examine and analyze the vast number of data flows and to correlate several other types of events from a cybersecurity perception. The supervision and categorization of network flow is an essential process not only for the scheduling, management, and regulation of the network’s services, but also for attacks identification and for the consequent forensics’ investigations. A serious potential disadvantage of the traditional software solutions used today for computer network monitoring, and specifically for the instances of effective categorization of the encrypted or obfuscated network flow, which enforces the rebuilding of messages packets in sophisticated underlying protocols, is the requirements of computational resources. In addition, an additional significant inability of these software packages is they create high false positive rates because they are deprived of accurate predicting mechanisms.
For all the reasons above, in most cases, the traditional software fails completely to recognize unidentified vulnerabilities and zero-day exploitations. This paper proposes a novel intelligence driven Network Flow Forensics Framework (NF3) which uses low utilization of computing power and resources, for the Next Generation Cognitive Computing SOC (NGC2SOC) that rely solely on advanced fully automated intelligence methods. It is an effective and accurate Ensemble Machine Learning forensics tool to Network Traffic Analysis, Demystification of Malware Traffic and Encrypted Traffic Identification.
An Intrusion Detection based on Data mining technique and its intended import...Editor IJMTER
Intrusion detection is a pivotal and essential requirement of today’s era. There are two
major side of Intrusion detection namely, Host based intrusion detection as well as network based
intrusion detection. In Host based intrusion detection system, it monitors the information arrive at the
particular machine or node. While in network based intrusion system, it monitor and analyze whole
traffic of network. Data mining introduce latest technology and methods to handle and categorize
types of attacks using different classification algorithm and matching the patterns of malicious
behavior. Due to the use of this data mining technology, developers extract and analyze the types of
attack in the network.
In addition to this there are two major approach of intrusion detection. First, anomaly based approach,
in which attacks are found with high false alarm rate. However, in signature based approach, false
alarm rate is low with lack of processing of novel attacks. Most of the researchers do their research
based on signature intrusion with the purpose to increase detection rate. Major advantage of this
system, IDS does not require biased assessment and able to identify massive pattern of attacks.
Moreover, capacity to handle large connection records of network. In this paper we try to discover
the features of intrusion detection based on data mining technique.
Survey on Host and Network Based Intrusion Detection SystemEswar Publications
With invent of new technologies and devices, Intrusion has become an area of concern because of security issues, in the ever growing area of cyber-attack. An intrusion detection system (IDS) is defined as a device or software application which monitors system or network activities for malicious activities or policy violations. It produces reports to a management station [1]. In this paper we are mainly focused on different IDS concepts based on Host and Network systems.
Cybercrime is increasing at a faster pace and sometimes causes billions of dollars of business- losses so
investigating attackers after commitment is of utmost importance and become one of the main concerns of
network managers. Network forensics as the process of Collecting, identifying, extracting and analyzing
data and systematically monitoring traffic of network is one of the main requirements in detection and
tracking of criminals. In this paper, we propose an architecture for network forensic system. Our proposed
architecture consists of five main components: collection and indexing, database management, analysis
component, SOC communication component and the database.
The main difference between our proposed architecture and other systems is in analysis component. This
component is composed of four parts: Analysis and investigation subsystem, Reporting subsystem, Alert
and visualization subsystem and the malware analysis subsystem. The most important differentiating
factors of the proposed system with existing systems are: clustering and ranking of malware, dynamic
analysis of malware, collecting and analysis of network flows and anomalous behavior analysis.
EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...IJNSA Journal
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
CYBER FORENSICS AND AUDITING
Topics Covered: Introduction to Cyber Forensics, Computer Equipment and associated storage, media Role of forensics Investigator, Forensics Investigation Process, Collecting Network based Evidence Writing, Computer Forensics Reports, Auditing, Plan an audit against a set of audit criteria, Information Security Management, System Management. Introduction to ISO 27001:2013
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
Detecting and Preventing Attacks Using Network Intrusion Detection SystemsCSCJournals
Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathway for intrusion. This system is designed to detect and combat some common attacks on network systems. It follows the signature based IDs methodology for ascertaining attacks. A signature based IDS will monitor packets on the network and compare them against a database of signatures or attributes from known malicious threats. It has been implemented in VC++. In this system the attack log displays the list of attacks to the administrator for evasive action. This system works as an alert device in the event of attacks directed towards an entire network.
Malicious activities (malcodes) are self replicating
malware and a major security threat in a network environment.
Timely detection and system alert flags are very essential to
prevent rapid malcodes spreading in the network. The difficulty
in detecting malcodes is that they evolve over time. Despite the fact
that signature-based tools, are generally used to secure systems,
signature-based malcode detectors neglect to recognize muddled
and beforehand concealed malcode executables. Automatic signature
generation systems has likewise been use to address the issue
of malcodes, yet there are many works required for good detection.
Base on the behavior way of malcodes, a behavior approach is
required for such detection. Specifically, we require a dynamic
investigation and behavior Rule Base system that distinguishes
malcodes without erroneously block legitimate traffic or increase
false alarms. This paper proposed and discussed the approach
using Machine learning and Indicators of Compromise (IOC) to
analyze intrusion in a network, to identify the cause of the attack
and to provide future detection. This paper proposed the use of
behaviour malware analysis framework to analyze intrusion data,
apply clustering algorithm on the analyzed data and generate IOC
from the clustered data for IOCRule, which will be implemented
into Snort Intrusion Detection System (IDS) for malicious code
detection.
A NOVEL HEADER MATCHING ALGORITHM FOR INTRUSION DETECTION SYSTEMSIJNSA Journal
The evolving necessity of the Internet increases the demand on the bandwidth. Therefore, this demand opens the doors for the hackers’ community to develop new methods and techniques to gain control over networking systems. Hence, the intrusion detection systems (IDS) are insufficient to prevent/detect unauthorized access the network. Network Intrusion Detection System (NIDS) is one example that still suffers from performance degradation due the increase of the link speed in today’s networks. In This paper we proposed a novel algorithm to detect the intruders, who’s trying to gain access to the network using the packets header parameters such as;
source/destination address, source/destination port, and protocol without the need to inspect each packet content looking for signatures/patterns. However, the “Packet Header Matching” algorithm enhances the overall speed of the matching process between the incoming packet headers against the rule set. We ran the proposed algorithm to proof the proposed concept in coping with the traffic arrival speeds and the various bandwidth demands. The achieved results were of significant enhancement of the overall performance in terms of detection speed.
Include at least 250 words in your posting and at least 250 words inmaribethy2y
Include at least 250 words in your posting and at least 250 words in your reply. Indicate at least one source or reference in your original post. Please see syllabus for details on submission requirements.
Module 1 Discussion Question
Search "scholar.google.com" for a company, school, or person that has been the target of a network
or system intrusion? What information was targeted? Was the attack successful? If so, what changes
were made to ensure that this vulnerability was controlled? If not, what mechanisms were in-place to protect against the intrusion.
Reply-1(Shravan)
Introduction:
Interruption location frameworks (IDSs) are programming or equipment frameworks that robotize the way toward observing the occasions happening in a PC framework or system, examining them for indications of security issues. As system assaults have expanded in number and seriousness in the course of recent years, interruption recognition frameworks have turned into an essential expansion to the security foundation of generally associations. This direction archive is planned as a preliminary in interruption recognition, created for the individuals who need to comprehend what security objectives interruption location components serve, how to choose and design interruption discovery frameworks for their particular framework and system situations, how to deal with the yield of interruption identification frameworks, and how to incorporate interruption recognition capacities with whatever remains of the authoritative security foundation. References to other data sources are likewise accommodated the peruse who requires particular or more point by point guidance on particular interruption identification issues.
In the most recent years there has been an expanding enthusiasm for the security of process control and SCADA frameworks. Moreover, ongoing PC assaults, for example, the Stunt worm, host appeared there are gatherings with the inspiration and assets to viably assault control frameworks.
While past work has proposed new security components for control frameworks, few of them have investigated new and in a general sense distinctive research issues for anchoring control frameworks when contrasted with anchoring conventional data innovation (IT) frameworks. Specifically, the complexity of new malware assaulting control frameworks - malware including zero-days assaults, rootkits made for control frameworks, and programming marked by confided in declaration specialists - has demonstrated that it is exceptionally hard to avert and identify these assaults dependent on IT framework data.
In this paper we demonstrate how, by joining information of the physical framework under control, we can distinguish PC assaults that change the conduct of the focused on control framework. By utilizing information of the physical framework we can center around the last goal of the assault, and not on the specific instruments of how vulnerabilities are misused, and how ...
Topic Since information extracted from router or switch interfaces.docxjuliennehar
Topic Since information extracted from router or switch interfaces to not provide specific evidence of a particular crime in most cases, what use is the information collected from these devices.
Read and respond to atleast two other students Discussions. (5-6 lines would be more sufficient)
#1.Posted by Srikanth
Routers and switches give the availability, both inside the demilitarized Zone (DMZ) environment and to different tareas of the system to which the DMZ is connected. This makes Routers and switches prime targets for hackers to exploit and gather data about the system or just use as springboards on other devices. This section presents data on the best way to information and arrange some significant router and switch security includes that enable run safely and ensure the devices that they associate. Routers direct traffic all through the undertaking system and are normally the first line of barrier when the system is associating with the Internet. Hackers try to infiltrate routers to gather data or use them as launching pads for further attacks. This is the reason it is critical to secure switches' management interfaces and services to make them trouble for an interloper to hack. Similarly as with routers, switches have an expanding job in system security. The switch gives numerous highlights, including port security. VLANs and PVLANs give the tools to keep the devices on the DMZ secure. It is additionally imperative to secure the switch's management interfaces and services with the goal that hackers can't break into the switch to change VLAN designs, change port settings, or utilize the switch to connect with different parts of the network.
Network forensics is capture, recording and analysis of network packets in order to determine the source of network security attacks. The major goal of network forensics is to collect evidence. It tries to analyze network traffic data, which is collected from different sites and different network equipment, such as firewalls and IDS. In addition, it monitors on the network to detect attacks and analyze the nature of attackers. Network forensics is also the process of detecting intrusion patterns, focusing on attacker activity.
Computer documents, emails, text and instant messages, transactions, images and Internet histories are examples of information that can be gathered from electronic devices and used very effectively as evidence. For example, mobile devices use online-based based backup systems, also known as the “cloud”, that provide forensic investigators with access to text messages and pictures taken from a particular phone. These systems keep an average of 1,000–1,500 or more of the last text messages sent to and received from that phone.In addition, many mobile devices store information about the locations where the device traveled and when it was there. To gain this knowledge, investigators can access an average of the last 200 cell locations accessed by a mobile device. Satellite navig ...
Running Head Security Assessment Repot (SAR) .docxSUBHI7
Running Head: Security Assessment Repot (SAR) 1
Security Assessment Report (SAR) 27
Intentionally left blank
Security Assessment Report (SAR)
CHOICE OF ORGANIZATION IS UNIVERSITY OF MARYLAND MEDICAL CENTER (UMMC) OR A FICTITIUOS ORGANIZATION (BE CREATIVE)
Introduction
· Research into OPM security breach.
· What prompts this assessment exercise in our choice of organization? “but we have a bit of an emergency. There's been a security breach at the Office of Personnel Management. need to make sure it doesn't happen again.
· What were the hackers able to do? OPM OIG report and found that the hackers were able to gain access through compromised credentials
· How could it have been averted? A) security breach could have been prevented, if the Office of Personnel Management, or OPM, had abided by previous auditing reports and security findings.b) access to the databases could have been prevented by implementing various encryption schemas and c) could have been identified after running regularly scheduled scans of the systems.
Organization
· Describe the background of your organization, including the purpose, organizational structure,
· Diagram of the network system that includes LAN, WAN, and systems (use the OPM systems model of LAN side networks), the intra-network, and WAN side networks, the inter-net.
· Identify the boundaries that separate the inner networks from the outside networks.
· include a description of how these platforms are implemented in your organization: common computing platforms, cloud computing, distributed computing, centralized computing, secure programming fundamentals (cite references)
Threats Identification
Start Reading: Impact of Threats
The main threats to information system (IS) security are physical events such as natural disasters, employees and consultants, suppliers and vendors, e-mail attachments and viruses, and intruders.
Physical events such as fires, earthquakes, and hurricanes can cause damage to IT systems. The cost of this damage is not restricted to the costs of repairs or new hardware and software. Even a seemingly simple incident such as a short circuit can have a ripple effect and cost thousands of dollars in lost earnings.
Employees and consultants; In terms of severity of impact, employees and consultants working within the organization can cause the worst damage. Insiders have the most detailed knowledge of how the information systems are being used. They know what data is valuable and how to get it without creating tracks.
Suppliers and vendors; Organizations cannot avoid exchanging information with vendors, suppliers, business partners, and customers. However, the granting of access rights to any IS or network, if not done at the proper level—that is, at the least level of privilege—can leave the IS or ne ...
Cyber Warfare is the current single greatest emerging threat to National Security. Network security has become an essential component of any computer network. As computer networks and systems become ever more fundamental to modern society, concerns about security has become increasingly important. There are a multitude of different applications open source and proprietary available for the protection +-system administrator, to decide on the most suitable format for their purpose requires knowledge of the available safety measures, their features and how they affect the quality of service, as well as the kind of data they will be allowing through un flagged. A majority of methods currently used to ensure the quality of a networks service are signature based. From this information, and details on the specifics of popular applications and their implementation methods, we have carried through the ideas, incorporating our own opinions, to formulate suggestions on how this could be done on a general level. The main objective was to design and develop an Intrusion Detection System. While the minor objectives were to; Design a port scanner to determine potential threats and mitigation techniques to withstand these attacks. Implement the system on a host and Run and test the designed IDS. In this project we set out to develop a Honey Pot IDS System. It would make it easy to listen on a range of ports and emulate a network protocol to track and identify any individuals trying to connect to your system. This IDS will use the following design approaches: Event correlation, Log analysis, Alerting, and policy enforcement. Intrusion Detection Systems (IDSs) attempt to identify unauthorized use, misuse, and abuse of computer systems. In response to the growth in the use and development of IDSs, we have developed a methodology for testing IDSs. The methodology consists of techniques from the field of software testing which we have adapted for the specific purpose of testing IDSs. In this paper, we identify a set of general IDS performance objectives which is the basis for the methodology. We present the details of the methodology, including strategies for test-case selection and specific testing procedures. We include quantitative results from testing experiments on the Network Security Monitor (NSM), an IDS developed at UC Davis. We present an overview of the software platform that we have used to create user-simulation scripts for testing experiments. The platform consists of the UNIX tool expect and enhancements that we have developed, including mechanisms for concurrent scripts and a record-and-replay feature. We also provide background information on intrusions and IDSs to motivate our work.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Network Intrusion Detection And Countermeasure Selection In Virtual Network (...ClaraZara1
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in detecting abnormal content in the traffic data during information passing from one node to another and also detects known attack signature and unknown attack. This approach is tested by running the artificial network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
These days the security provided by the computer systems is a big issue as it always has the threats of
cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security
provided by the blue team operations tends to be costly if done in large firms as a large number of systems
need to be protected against these attacks. This leads these firms to turn to less costly security
configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services
provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS
attacks which are done on both physical and network layers. These attacks in turn result in loss of
extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it,
and taking appropriate action to not only stop the attack but also locate its source IP address. This whole
process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will
result in not only the detection of the attack but also the source IP address of the machine on which the
attack is initiated and completed. The end product of this research will result in sets of default rules for the
Snort tool which will not only be able to provide better security than its previous versions but also be able
to provide the user with the IP address of the attacker or the person conducting the attack. The system
involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata
which is another intrusion detection system capable of detecting all these types of attacks as mentioned.
Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to
be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for
traffic monitoring and incident response than any other of its alternatives in the market. Since this system
is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as
it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS
automation in its capability to capture and report the malicious packets found during the network scan. All
of this gives the IDS a capability of a low budget automated threat detection system. This paper gives
complete guidelines for authors submitting papers for the AIRCC Journals.
Network security is a dynamic art, with dangers appearing as fast as black hats can exploit vulnerabilities. While there are basic “golden rules” which can make life difficult for the bad guys, it remains a challenge to keep networks secure. John Chambers, Executive Chairman of Cisco, famously said “there are two types of companies: those that have been hacked, and those who don’t know they have been hacked”. The question for most organizations isn’t if they’re going to be breached, but how quickly they can isolate and mitigate the threat. In this paper, we’ll examine best practices for effective cybersecurity – from both a proactive (access hardening) and reactive (threat isolation and mitigation) perspective. We’ll address how network automation can help minimize cyberattacks by closing vulnerability gaps and how it can improve incident response times in the event of a cyberthreat. Finally, we’ll lay a vision for continuous network security, to explore how machine-to-machine automation may deliver an auto-securing and self-healing network.
Go to www.esgjrconsultinginc.com
Toward Continuous Cybersecurity With Network AutomationKen Flott
Network security is a dynamic art, with dangers appearing as
fast as black hats can exploit vulnerabilities. While there are
basic “golden rules” which can make life difficult for the bad
guys, it remains a challenge to keep networks secure. John
Chambers, Executive Chairman of Cisco, famously said “there
are two types of companies: those that have been hacked, and
those who don’t know they have been hacked”. The question
for most organizations isn’t if they’re going to be breached, but
how quickly they can isolate and mitigate the threat.
In this paper, we’ll examine best practices for effective
cybersecurity – from both a proactive (access hardening)
and reactive (threat isolation and mitigation) perspective.
We’ll address how network automation can help minimize
cyberattacks by closing vulnerability gaps and how it can
improve incident response times in the event of a cyberthreat.
Finally, we’ll lay a vision for continuous network security, to
explore how machine-to-machine automation may deliver an
auto-securing and self-healing network.
Security and Ethical Challenges Contributors Kim Wanders.docxedgar6wallace88877
Security and Ethical Challenges
Contributors: Kim Wandersee, Les Pang
Computer Security
Computer Security Goals
Computer security must be viewed in a holistic manner and provide an end-to-end protection
as data moves through its lifecycle. Data originates from a user or sensor, passes over a
network to reach a computing system that hosts software. This computer system has software
and processes the data and stores in in a storage device. That data is backed up on a device
and finally archived. The elements that handle the data need to be secure. Computer security
pertains to all the means to protect the confidentiality, integrity, availability, authenticity,
utility, and possession of data throughout its lifecycle.
Confidentiality: A security principle that
works to ensure that data is not disclosed to
unauthorized persons.
Integrity: A security principle that makes sure
that information and systems are not
modified maliciously or accidentally.
Availability: A security principle that assures
reliable and timely access to data and
resources by authorized individuals.
Authenticity: A security principle that the
data, transactions, communications or
documents are genuine, valid, and not
fraudulent.
Utility: A security principle that addresses
that the information is usable for its intended
purpose. .
Possession: A security principle that works to
ensure that data remains under the control of
the authorized individuals.
Figure 1. Parkerian Hexad (PH) security model.
The Parerian Hexad (PH) model expands on the Confidentiality, Integrity, and Availability (CIA)
triad that has been the basic model of Information Security for over 20 years. This framework is
used to list all aspects of security at a basic level. It provides a complete security framework to
provide the means for information owners to protect their information from any adversaries
and vulnerabilities. It adds Authenticity, Utility, and Possession to CIA triad security model. It
addresses security aspects for data throughout its lifecycle.
The Center for Internet Security has identified 20 controls necessary to protect an organization
from known cyber-attack. The first 5 controls will provide effective defense against the most
common cyber-attacks, approximately 85% of attacks. The 5 controls are:
1. Inventory of Authorized and Devices
2. Inventory of Authorized and Unauthorized Software
3. Secure Configurations for Hardware and Software
4. Continuous Vulnerability Assessment and Remediation
5. Controlled User of Administrative Privileges
A full explanation of all 20 controls is available at the Center for Internet Security website.
Search for CIS controls.
Security Standards and Regulations
The National Institute of Standards and Technology (NIST), Computer Security Division, provides
security standards in its Federal Information Processing Standards (.
Security and Ethical Challenges Contributors Kim Wanders.docxfathwaitewalter
Security and Ethical Challenges
Contributors: Kim Wandersee, Les Pang
Computer Security
Computer Security Goals
Computer security must be viewed in a holistic manner and provide an end-to-end protection
as data moves through its lifecycle. Data originates from a user or sensor, passes over a
network to reach a computing system that hosts software. This computer system has software
and processes the data and stores in in a storage device. That data is backed up on a device
and finally archived. The elements that handle the data need to be secure. Computer security
pertains to all the means to protect the confidentiality, integrity, availability, authenticity,
utility, and possession of data throughout its lifecycle.
Confidentiality: A security principle that
works to ensure that data is not disclosed to
unauthorized persons.
Integrity: A security principle that makes sure
that information and systems are not
modified maliciously or accidentally.
Availability: A security principle that assures
reliable and timely access to data and
resources by authorized individuals.
Authenticity: A security principle that the
data, transactions, communications or
documents are genuine, valid, and not
fraudulent.
Utility: A security principle that addresses
that the information is usable for its intended
purpose. .
Possession: A security principle that works to
ensure that data remains under the control of
the authorized individuals.
Figure 1. Parkerian Hexad (PH) security model.
The Parerian Hexad (PH) model expands on the Confidentiality, Integrity, and Availability (CIA)
triad that has been the basic model of Information Security for over 20 years. This framework is
used to list all aspects of security at a basic level. It provides a complete security framework to
provide the means for information owners to protect their information from any adversaries
and vulnerabilities. It adds Authenticity, Utility, and Possession to CIA triad security model. It
addresses security aspects for data throughout its lifecycle.
The Center for Internet Security has identified 20 controls necessary to protect an organization
from known cyber-attack. The first 5 controls will provide effective defense against the most
common cyber-attacks, approximately 85% of attacks. The 5 controls are:
1. Inventory of Authorized and Devices
2. Inventory of Authorized and Unauthorized Software
3. Secure Configurations for Hardware and Software
4. Continuous Vulnerability Assessment and Remediation
5. Controlled User of Administrative Privileges
A full explanation of all 20 controls is available at the Center for Internet Security website.
Search for CIS controls.
Security Standards and Regulations
The National Institute of Standards and Technology (NIST), Computer Security Division, provides
security standards in its Federal Information Processing Standards ( ...
Insight Brief: Security Analytics to Identify the 12 Indicators of Compromise21CT Inc.
In this security insight brief, 21CT researchers look at the malicious network behaviors that concern organizations the most, and how to use security analytics to find them before damage is done. Understanding these 12 indicators of compromise are critical to identifying a network breach.
Collecting and analyzing network-based evidenceCSITiaesprime
Since nearly the beginning of the Internet, malware has been a significant deterrent to productivity for end users, both personal and business related. Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply ‘witness’ of a criminal event. Forensic investigations include collection, recovery, analysis, and presentation of information stored on network devices and related to network crimes. These activities often involve wide range of analysis tools and application of different methods. This work presents methods that helps digital investigators to correlate and present information acquired from forensic data, with the aim to get a more valuable reconstructions of events or action to reach case conclusions. Main aim of network forensic is to gather evidence. Additionally, the evidence obtained during the investigation must be produced through a rigorous investigation procedure in a legal context.
Layered Approach for Preprocessing of Data in Intrusion Prevention SystemsEditor IJCATR
Due to extensive growth of the Internet and increasing availability of tools and methods for intruding and attacking
networks, intrusion detection has become a critical component of network security parameters. TCP/IP protocol suite is the defacto
standard for communication on the Internet. The underlying vulnerabilities in the protocols is the root cause of intrusions. Therefor
Intrusion detection system becomes an important element in network security that controls real time data and leads to huge
dimensional problem. Processing large number of packets and data in real time is very difficult and costly. Therefor data preprocessing
is necessary to remove redundant and unwanted information from packets and clean network data. Here, we are focusing on
two important aspects of intrusion detection; one is accuracy and other is performance. The layered approach of TCP/IP model can be
applied to packet pre-processing to achieve early and faster intrusion detection. Motivation for the paper comes from the large impact
data preprocessing has on the accuracy and capability of anomaly-based NIPS. In this paper it is demonstrated that high attack
detection accuracy can be achieved by using layered approach for data preprocessing in Internet. To reduce false positive rate and to
increase efficiency of detection, the paper proposed framework for preprocessing in intrusion prevention system. We experimented
with real time network traffic as well as he KDDcup99 dataset for our research.
Modern information security management best practices dictate that an enterprise assumes full
configuration control of end user computer systems (laptops, deskside computers, etc.). The benefit of this
explicit control yields lower support costs since there are less variation of machines, operating systems,
and applications to provide support on, but more importantly today, dictating specifically what software,
hardware, and security configurations exist on an end user's machine can help reduce the occurrence of
infection by malicious software significantly. If the data pertaining to end user systems is organized and
catalogued as part of normal information security logging activities, an extended picture of what the end
system actually is may be available to the investigator at a moment's notice to enhance incident response
and mitigation. The purpose of this research is to provide a way of cataloguing this data by using and
augmenting existing tools and open source software deployed in an enterprise network.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
4777.team c.final
1. Tracking Exploitations Using Digital Forensics: An Exercise of Cybersecurity Utilizing
Vulnerabilities
Ian Bernasconi
Florida State
University
iab17b@my.fsu.edu
Michael Costello
Florida State
University
mvc09@my.fsu.edu
Alexis Harvey
Florida State
University
aph15@my.fsu.edu
Kody Horvath
Florida State
University
kjh15b@my.fsu.edu
Sayvion Mayfield
Florida State
University
sm16bc@my.fsu.edu
Abstract
Cybersecurity, the constant challenge of protecting internet-connected devices from tampering, theft, or
damage, creates an endless cycle of prevention,patching, and evaluation of systems. This is an important problemfor
businesses,organizations, and personal networks due to the expectations of confidentiality, integrity, and availability
of devices and information. Losing control of systems or customer information can be costly, pushing security to be
one of the top priorities of any company in order to maintain constant uptime. While many attacks on organizations
can be catastrophic and lead to damage of systems and information, there are also subtle, quiet attacks that without
close monitoring might never be detected. Because of this, constant monitoring of data and major systems to detect
changes in confidential files and tasks are essentialto maintaining a secure and working network. Using many tools
within Linux environments combined with the defenses on our network, we aim to illustrate the importance of data
forensics on a network and go through the process of not only monitoring a systembut displaying how we can detect
an attack on vital systems.
Keywords: Digital forensics, Intrusion detection,network security, honeypot
Introduction
A network of computers and systems,the backbone for any company or organization, is what allows many
companies to operate normally in a age where processing and storage of large amounts of data is important. Because
of this, maintaining an environment clear of malicious code, insider and outsider attacks, and all other threats is one
of the biggest concerns of many businesses.While we allow for defensive systems to detect attacks, there is always a
possible threat from the inside or by more silent outsiderattacks. Therefore, it is important that any business handling
records and important data participate in data forensics to make sure that any unnoticed changes are reversed,and that
in case of an attack, proper steps are taken to ensure the attackers can no longer access the system, and to determine
the extent of the damage.
We are adopting a three-pronged strategy in this exercise. Our focus will center around forensics and will
incorporate several tools to generate data on any attempts to breach the network. By collecting and preserving digital
evidence of intrusion attempts,we will be able to build a report on any malicious activity. We will employ HoneyBOT
to capture malicious traffic and ensnare othergroups’attempts at breaches. We will also build a sophisticated forensics
systemusing the SecurityOnion tool set, giving us access to a number of powerful IDS tools. Attackers often leave
backdoors, or other traces, during infiltration; and having a complete set of logs is critical to understanding how the
breach occurred, and how to prevent an intrusion in the future.
While it is important to be able to determine the extent of a breach, and identify the malicious actors, this
information is useless if you are unable to prevent an attack from happening again in the future. Our defensive strategy
2. involves using a comprehensive set of firewall rules within the Palo Alto Network Firewall interface, as well as the
open source firewall program pfSense. Our initial setup will behave similarly to a typical network, and as we collect
evidence of intrusion attempts, both successful and unsuccessful, we will tweak our firewall rules to harden our
security. Firewall rules should create a DMZ in the network architecture, allowing outside requests to interface with
relevant machines, while still shielding critical assets frommalicious attacks.
After ensuring that our own network is properly protected and server logs are accurate, we can turn ourfocus
onto attacking the rival entities. The first objective is to run some form of reconnaissance on the opponent’s network.
We have access to Wireshark in our virtual environment, which will allow us to analyze packets being sent and
received across their networks. After gleaning enough information about the critical infrastructure, ourattack strategy
will shift in focus to using an LLMNR and NetBIOS exploit to perform a Responder Attack, as well as to create
backdoors in opponents’ critical network architecture. Kali Linux has usefultools for launching these attacks and will
be an invaluable resource throughout this exercise.
Cybersecurity Forensics
Network Forensic Analysis Tools
In order to convey the best practices for network traffic forensics analysis it is important to understand that
forensics is a tedious and time-consuming task. There are many tools out there that will allow you to view the network
traffic in real-time but viewing the network traffic for larger organizations becomes very heavy. Corey, Peterman,
Shearman, Greenberg, Van Bokkelen (2002) stated that many times to analyze the traffic on a larger-scale network it
is best to archive the traffic and analyze the subsets that are deemed appropriate. This is a process best known as
reconstructive traffic analysis, or network forensics (Corey et al., 2002). An example of this would include “the
analysis detecting a user account and its Pretty Good Privacy keys being compromised, good practice requires you to
review all subsequent activity by that user, or involving those keys” (Corey et al., 2002). There are a wide variety of
reasons to want to better understand the network traffic, however, legal and security concerns are always considered
to be to priority. Some of the more low-level reasons would include mail servers losing a large number of messages
and the backup methods failing. A fix to this would be analyzing the traffic and finding the lost messages through the
recorded traffic.
A topic that is typically brought up when talking about and researching Network Forensic Analysis Tools
(NFAT) is their purpose alongside Intrusion Detection Systems (IDS). Firewalls and IDS’s are great resources for
network security, but a question that arises when NFAT’s are introduced to them is are they working to complement
each other or are they being replaced? A regular IDS’s job is to detect activity that violates an organization's security
policy by implementing a set of rules describing preconfigured patterns of interests. A firewall’s job is to allow or
disallow traffic to or from specific networks, machine addresses, and port numbers. The general consensus is that
NFAT’s work together with firewalls and IDS’s by preserving a long-term record of network traffic, and it allows
quick analysis of trouble spots.There are three major tasks that NFAT’s must perform well: capture network traffic,
analyze the traffic according to the user’s needs,and must let systemusers discoverusefuland interesting things about
the analyzed traffic.
When analyzing the traffic, it is best to archive the network traffic first which is the first layer of forensic
information. There is a method called sessionizing and is extremely useful for filtering unrelated packets that may
have been transmitted at the same time as the packets you need to inspect. The tool should structure the packets into
individual transport-layer connections between machines (Corey et al., 2002). There is also protocol parsing and
analysis which is typically done by hand. A list of queries is typed in to make this happen: tcpdump, strings, grep
(specific word or phrase), and when completed researchers can rerun tcpdump with a filter to extract from data. Now
the more efficient approach to uncovering all of this data is expert-system analysis on the sessionized traffic. This
3. approach evaluates the individual connections content and also it correlates the connections with each other. Using
forensic tools such as NetIntercept, would let you explore and understand data that was unintelligible at the packet-
sniffer level (Corey et al., 2002).
There are certain specific security concerns when working with NFAT’s such as handling encrypted traffic,
avoiding detection and circumvention, and protecting the sensitive data revealed by the analysis. There are programs
and documents that will help you secure your systemand ensure all three concerns mentioned before will be covered.
To avoid detection, L0pht Heavy Industries introduced a program called antisniff, which attempts to find other
machines running packet monitors (sniffers). The program looks for certain abnormal behaviors demonstrated by
common NT and Unix TCP stacks while sniffers are running. When protecting the data, you must remember that all
of the packets and their contents are available to anyone with physical access to the same wire unless encrypted.
Computers that are being used to perform the network forensics are most secure when users can access them only
from their consoles,but you could also multihome the machine, with a silent interface on the monitored networks and
an interactive one on a private network with access limited by policy or physical barriers (Corey et al., 2002).
Big Data Analysis
The use of big data analysis of network traffic to find threats have become increasingly more sought after
and researched. To understand the use of big data in cyber security, it is important to know what big data is. The term
Big Data refers to exceptionally large data sets of analysis and management technologies that that surpass the
capabilities of traditional data processing technologies,that reveal patterns,relations, and trends.These big data tools
and cybersecurity solutions has led to the creation of the term ‘Big Data Cybersecurity Analytic Systems’, “which
refers to systems that collect large amount of security event data from different sources and analyze it using big data
tools and technologies for detecting attacks either through attack pattern matching or identifying anomalies ,”(Ullah &
Babar, 2018). Some of the sources from which the data is obtained from other than network traffic data, include
firewall logs, web logs, system logs and application logs. The big data analysis on network traffic data is based on
detecting anomalous activities and malicious data that are transmitted over the network, by analyzing the large
quantities of network traffic with big data tools.
“It has been proposed that big data tools would transform cybersecurity analytics by first, enabling
organizations to collect a large amount of heterogeneous data from diverse sources such as networks, databases,and
applications. Second, perform deep security analytics at real-time. Third, it would provide a consolidated view of the
security-related information,”( Ullah & Babar, 2018). Big data can be used against various types of online threats.
Network vulnerabilities are determined by big data by analyzing the network and determining which databases are
vulnerable to hackers. This is crucial for databases that have sensitive information. “Big data has the ability to detect
anomalies in a network, without knowing what kind of attributes to look for at the start of the analysis” (Hess 2018).
This is usually done by finding correlations in large data sets,or mining and analyzing the data set to find patterns and
behaviors.Anomalies are also major with behavior of an attacker. Analysis of irregular behaviors can help determine
and protect against future threats, such as attackers installing malicious code, or sending a malicious email with a
Trojan horse malware. Big data has made many improvements in cybersecurity and provided new options to analyze
threats from solutions based on analysis. Understanding the strategies of big data can help avoid breaches and fo rm
more efficient protection methods.
It is often wondered how Big data differs from the conventional approaches of network traffic analysis,
systemlogs, and other sources that identify threats and malicious activities. “The main differences that are reported
are the tools to control large quantities of structured and unstructured data” (Cárdenas, Manadhata, & Rajan 2013).
Though analyzing logs and network traffic for forensics and intrusion detection is already a thing, the traditional
technologies aren’t always the most efficient. This is because they weren’t equipped to handle large quantity data sets
4. for long periods of time. However, new big data technologies are becoming part of security management software
because they help clean and organize the incomplete, heterogeneous data efficiently (Cárdenas, Manadhata,& Rajan
2013). Big data has made managing large-scale collection and storage of data possible,thus expanding the amount of
information collected about threats to the network. Technologies such as Hadoop have incorporated big data analysis
and have shown to handle data more quickly and efficiently than traditional technologies, which don’t have the
resources to handle large amounts of data. The security data warehouse behind Hadoop “lets users mine meaningful
security information from not only firewalls and security devices but also web traffic, business processes,and other
daily transactions,” (Cárdenas, Manadhata, & Rajan 2013).
Though big data has many advantages,it also comes with disadvantages.big data has provided more threats,
such as attackers that use big data to discover new holes in a network. (Hess 2018). Some of the main threats that
come with using big data are protecting sensitive and personal data, data rights, and not having the skill or ability to
analyze the data, like a data scientist. When your security around your big data is low, you have a high chance that
attackers will see big data sets and will be much more intrigued to hack yoursystem. However, if the proper steps are
taken and your big data is properly managed and protected there are more benefits than threats.Big data has provided
the opportunity to analyze sources of data and properly respond in real time. Big data can also analyze vast amounts
of data and make connections that traditionaltechnology wouldn’t otherwise generate.Big data provides management
of real-time network traffic and detection of malicious and suspicious patterns and provide overall enhanced security
techniques.
Network Topology & Firewall Defense
Within our lab environment, there are a total of 5 main devices for each team member being used for testing
and research. The devices are as follows: (1) An Apache 2.2 equipped Windows virtual machine, (2) Windows 10
virtual machine, (3) a Kali Linux machine, (4) a Raspberry Pi and (5) a Ubuntu machine. The Kali Linux machine
will be our main machine for penetration and network scanning. In addition, Security Onion and Comodo in trusion
detection systems will be used to help monitor the network. Lastly, we will be utilizing Palo Alto and pfSense equipped
machines for firewall exceptions and rulemaking for ourenvironment. All of these devices were connected within one
subnet, the 192.168.72.0 network, but most are now configured behind firewalls on the 172.16.0.0 subnet. The
machine's respective IP addresses, Domain Name System (DNS), and default gateways are listed in Figure 1. The
Ubuntu,Windows 7, Apache,and Raspberry Pi systems are all connected through the Palo Alto and pfSense interfaces.
The Onion Defense, Comodo, and Kali virtual machines are outside the firewall and trunk alongside the firewall
interface towards the rest of the FSU network from our SECNET Lab node.
Machine IP Address Subnet Mask
(CIDR)
DNS Default Gateway
Palo Alto Firewall 1) 192.168.74.114
192.168.72.114
172.16.31.254
2) 192.168.74.115
192.168.72.115
172.16.32.254
/24 192.168.72.7 192.168.74.114
pfSense 172.16.30.254 /24 192.168.72.7 192.168.74
Ubuntu 172.16.30.0/24 /24 192.168.72.7 172.16.30.254
6. Kali Linux Attacks
Responder Attack
Responderis an attack tool created by Trustwave SpiderLabs that can answer LLMNR and NBT-NS queries
giving its own IP address as the destination forany hostname requested.The responderattackis an attack used in Kali
Linux targeting a Windows machine who cannot resolve a hostname using DNS and instead relying on the Link-Local
Multicast Name Resolution (LLMNR) protocol to ask neighboring computers. The LLMNR can be used to resolve
both IPv4 and IPv6 addresses. In the event the LLMNR fails, NetBIOS Name Service (NBT-NS) will kick in and
resolve only IPv4 addresses.When these two protocols,LLMNR & NBT-NS, are used and host on the network who
knows the IP of the host being asked about can reply. The reply does not have to be correct but will still be regarded
as legitimate.
When initiating the attack, it is always best to see the options included with the attack and for Responderyou
can do this by simply typing “responder -h.” You must first specify the interface you wish to run the attack on such
as eth0. This will continue to run in the background listening for events to take place and when the client tries to
resolve a name not in the DNS, Responder will poison the LLMNR and NBT-NS requests that are sent out. For
example, when using the file explorer and requesting access to a network resource that is not there the attack will take
place. A usercan simply type “fielshare” which is not a valid resource and Responderwill take over and say that its
IP is the location of “fielshare.” The Windows machine will then try to connect to this resource using SMB which it
believes is located on the Kali host.The SMB process will send the Windows username and hashed password to the
Kali host.
Responder Attack (WPAD)
Responderhas been known to be more reliable in gaining usernames and password hashes through theWPAD
protocol. When a browser such as Internet Explorer is configured to automatically detect proxy settings,then it will
make use of the WPAD protocol to try and locate and download the wpad.dat Proxy Auto-Config (PAC) file. The
PAC files defines proxy servers that a web browser should use fordifferent URLs. The WPAD protocol works through
attempting to resolve the hostname “wpad” through a series of name requests. Fortunately, Internet Explorer has
WPAD enabled by default.
In order to initiate this attack, you must type the following argument, “responder-I eth0 -w -f”, in order to
poison WPAD requests and serve a valid wpad.dat PAC file. When a user on the local network uses Internet Explorer,
the browser should retrieve the wpad.dat file from Responder. With the argument -F, Responder will also force the
client to authenticate when they try to request the wpad.dat file. As the attack is performed from the local network,
Internet Explorer should recognize the service as being in the Intranet security zone and automatically provide the
user’s credentials without any prompt from the user. Both Internet Explorer and Google Chrome will automatically
do this, but Firefox prompts the userto manually enter their credentials, which is something to keep in mind when it
comes to a network with Firefox users.Wireshark can be used for both ways of the Responderattack to analyze and
ensure the attack was successful.
Captured Hashes in Responder Attacks
For both ways of the Responderattack, the hashes are output into the log files of Responder. In most cases,
hackers will use John the Ripper to crack the hashed passwords and gain access to the networks. This technique when
7. used during penetration tests have been quite successfuland many times credentials for Domain Admin accounts have
been captured and cracked. This leads to the compromise of the entire Active Directory domain and its resources.
Figure 3: Outputting Captured Hash Log
After running the responderattack on the network, we are able to output the results of our logs and display
the captured credential hashes for other computers. In this instance, we have the password hash of the machine with
the hostname “4777A04WIN” with the username admin. With this, we can use the several available programs that
will attempt to crack the hash using wordlists. Some of the most popular are hashcat and john the ripper.
Figure 4: Using John the Ripper to crack hashes
8. For cracking, we use John the Ripper which is a popular program used to for its simplicity and auto-hash
type detection. In this case, we are cracking a hash under the NTLMv2 protocol, which are based on challenge and
response.To crack, we specify the log file that contains ourhash and use the pre-defined wordlist “rockyou.txt”. This
list contains passwords (which have their own unique hashes) to compare to our unknown hash. While weaker
passwords can be cracked easily in minutes, stronger passwords can be hacked with more time and bigger wordlists.
Figure 5: Cracked Passwords
After running John the Ripper, we are given the password “Domainsup3r!” allowing us access into accounts
of the other machines. With the username and password exposed, this not only allows experienced hackers to take
control ofan entire machine, but also an entire domain if this computer is part of an organizational domain. This would
expose not only the original computer to hijacking, but all computers on the domain, potentially leading to issues with
data integrity, availability of machines and information, and destruction of systems.
Building Back Doors
With the usernames and passwords gleaned from John the Ripper, we are able to access accounts with admin
privileges. One valuable tool for an attacker to use is a back door. With access to an account with admin privileges,
it is possible to create another account with admin level privileges, allowing the attacker to reenter the systemwith
ease. An unsuspecting network administrator may not notice the new account created and may not be able to see
exactly what activities it is performing, especially if the account has an inconspicuous name. An effective counter
defense to this type of attack is routine account and access auditing and suspending login abilities to questionable
accounts.
Another common type of backdoor typically used by Advanced Persistent Threat actors is the use of a
malicious shell within an administrator privileged account. The creation of this shell allows an attacker to execute
commands on the host machine using a privileged account. This shell would be able to be accessed by the attackers
without the need of infiltrating the privileged account again. These shells can be used to modify or exfiltrate existing
sensitive data, or to perform some other action useful to the attacker. The use of these shells has a few hallmark
patterns that make it easier to detect their presence. Behavior analysis can be a relatively easy way to detect the
presence of the unauthorized shell, since it will behave inconsistently with legitimate use.
Solutions to Responder Attack
For this attack a solution provided by 4armed.com explains it is best to disable LLMNR and NBT-NS. Also,
to mitigate the WPAD attack, you can add an entry for “wpad” in your DNS zone. As long as the queries are resolved,
the attack will be prevented. To disable LLMNR you must navigate to the Local Group Policy Editor and then to
Computer Configuration->Administrative Templates->Network->DNS client. Locate “Turn off multicast name
resolution” and click “policy setting.” Enable the option, press Apply then click OK.
9. To disable NBT-NS, browse to DHCP scope options. Right click “Scope Options” and click “Configure
Options” in the Scope Options window, click on the Advanced tab, change the drop-down menu to “Microsoft
Windows 2000 Options” and select the “001 Microsoft Disable NetBIOS Option” and change its value to “0x2”, click
Apply and then OK.
Nmap
Nmap is an open source tool for networking security used by most operating systems,including Kali Linux.
Nmap is used in ways of determining things like what hosts are available on a network, what services they provide,
what firewalls are in use, and what OS is being used.Nmap can be used as an attack and a defense when deploying it
to a network. When using it as an attack tool, it can do anything from DoSing a target to exploiting them. The Nmap
scripts cover multiple categories: Auth is used to test if you can bypass authentication mechanism, Brute is used for
password guessing,Exploit is used to exploit a vulnerability, Dos is used to test whethera target is vulnerable to DoS,
etc. One of the simplest features for exploiting a network is running a scan on the target network to see if there are
any open ports that can be exploited; Having too many ports open is a major vulnerability on a system.
Figure 6: NMAP Scanning
Nikto
Nikto is an open source web server scanner tool that performs tests against hosts. The scanner looks for
dangerous files, it checks for older or outdated versions of servers, and specific issues on servers. In addition, Nikto
will look at the configuration of servers based on their presence of certain index files, and server options.Likewise,
the scanner will look for or find what software is installed on web servers.Once scanning is completed, Nikto has nice
10. features such as saving reports in HTML or XML format. Next, after interpreting the web server’s security holes,
preventative measures can be taken for protecting a web server like closing certain ports.Ultimately, Nikto can expose
potential vulnerabilities within web servers for the perspective web server admin or malicious hacker. In order to
perform an attack on a web server, you will need the following parameters shown in the figure below. In the first
command, the main components are an IP host,the output directory, and the format type for the report. For this scan,
we used an Internet Information Services (IIS) equipped Windows 10 web server. Once Nikto completes scanning,it
lists all the potential security holes on the web server. With the list of information provided, an administrator can
research and implement possible solutions,so the web server is not exploited. Nikto can be observed in action with a
firewall or network sniffing tool like Wireshark.
Figure 7: Nikto Scanning for vulnerabilities
Kali Linux Defenses
Palo Alto Firewall Defense
Palo Alto Networks, a world-renowned cybersecurity organization that offers advanced enterprise firewalls
and cloud-based solutions, created the first next-generation firewalls that can operate on and inspect all layers of
traffic. To demonstrate a controlled network environment, we needed one of the most basic forms of defense to
segment our network into trusted and untrusted zones. Because we are operating in a virtual network, were able to
achieve this using the Palo Alto virtual firewall solution. Using Palo Alto firewalls, we can define rules that allows us
to shape and verify traffic to and from other machines on the network and try to prevent other teams’ potentially
malicious machines from accessing our private network. We have implemented two Palo Alto firewalls to allow us
flexibility in the design of our network.
11. Figure 8: Palo Alto Interfaces
Because we are using the virtual version of the Palo Alto firewall to create our private and secure network,
we can access the management GUI through the management IP address 192.168.74.114 from our machine where we
can see the interfaces that are currently set up. On ethernet1/1, we have assigned the interface to the 192.168.72.0/ 24
subnet and tagged it as an untrusted layer 3 zone, along with ethernet1/2 as an interface in the 172.16.0.0/24 subnet,
tagged as a trusted layer 3 zone. This allows us to put all of our virtual machines behind the 172.16.0.0/24 network,
isolating our machines from the public 192.168.72.0/24 network. Through the use of the virtual router, we can route
traffic between the interfaces, which will eventually allow us to access the internet as well as other services.
12. Figure 9: Palo Alto Security Rules
With network traffic routed correctly, we created rules to designate what traffic was allowed in and out of
the firewall. Because we have no reason to add restrictions on our machines, we simply created a rule to allow
anything from the trusted zone (behind the firewall) to the untrusted zone (outside network), giving our machines
full internet access,as well as access to any services or websites. Because we only created one rule from the trusted
zone to the untrusted zone, traffic from machines outside the firewall is blocked from entering, giving us a layer of
protection incase this traffic is malicious.
Palo Alto Forensics
With rules in place, we can now monitor data inside and outside of the firewall, giving us a view of how
data is flowing between zones, what application or service the data is from, and which rules allowed or denied the
traffic. With this, we are given a log of all data, meaning if an attack on our firewall was to take place, we can set the
firewall to not only alert us, but also intelligently mark the attempt as a threat automatically. With this, we can
investigate the event,fix the vulnerability or hole in our firewall that is being attacked, and further prevent any
issues.In a serious environment such as an organization, major attacks on these systems would be flagged and the
location and origin of these attacks could be investigated as a crime and reported to authorities, especially if access
was eventually gained.
13. Figure 10: Palo Alto Monitoring Data and Traffic
Security Onion
The Security Onion suite is a Linux distribution loaded with powerful forensic tools. These tools can be used to
identify the potential attackers attempting to gain access to systemresources. In addition to keeping complete logs of
users attempting to log in to a protected website, it logs all of their activities and notifies you of possibly illegitimate
entry attempts. The suite comes complete with comprehensive IDS tools and NSM tools.
Honeypots
Honeypots are one type of defense a user can implement for their networks. The difference between a
honeypot and most defense mechanism is that most are made to keep the attackers out, while honeypots are made to
attract the attackers. A honeypot is a deflect systemthat is made to mimic a real computer system, in which attackers
interact with thinking they are attacking the target system; A honeypot is a computer security mechanism set to deflect
or counteract attempts at unauthorized use of computer systems. Honeypots are made to gather information and
behavior about an attacker while keeping the attacker from exploiting the real network. They can help further the
information you gather on the attacker’s behavior in more detail without disruption to your own network. There are
different interaction levels for honeypots,high-interaction,medium-interaction, and low-interaction. Low-interaction
gives the attacker very limited access to the operating system. There will be a small amount of internet protocol and
network services deployed to the system, just enough to deceive the attacker. High-interaction honeypots are much
more interactive. In addition to mimicking protocols, the attacker has a real system they can attack, making it less
14. likely for them to know it’s a decoy. Information gathered from high-interaction honeypots are also much more in-
depth and make it easier to spot threats, though they take much more time and resources to deploy.
The PenTBox is a tool on Kali Linux that can be used to implement a honeypot.To deploy the Honeypot, it
first must be run with root privileges. Then you can deploy the honeypot to run on a network, such as port 80.
Figure 11: Setting up the web server honeypot
When the attacker attempts to access the IP of the honeypot network on the server, they will get an “Access
Denied” message, leading them to think there is something important hidden on the network.
Figure 12: Attacker accessing the honeypot webpage on port 80
While on the otherend, the authorized user receives intrusion attempt messages with details of the intrusion.
If the attacker continued to exploit the honeypot server,the user will receive more alerts and details of the attacker’s
behavior.
f
Figure 13: Intrusion detection message
15. Works Cited
Corey, V., Peterman, C., Shearman, S., Greenberg, M. S., & Van Bokkelen, J. (2002). Network Forensic
Analysis. On the Wire.
Cárdenas, A.A., Manadhata, P.K., & Rajan, S.P. (2013). Big Data Analytics for Security. IEEE Security &
Privacy, 11, 74-76.
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