This document describes a proposed approach for anomaly detection in intrusion detection systems using outlier detection. It begins with background on intrusion detection systems and issues with existing approaches. It then presents the proposed two-stage approach using outlier detection: 1) Training with large normal datasets in a distributed storage environment, and 2) Testing intrusion datasets to compute an error value compared to the trained model. If the error value exceeds a threshold, the test data is flagged as anomalous. Experimental results on network packet datasets demonstrate the approach can effectively identify anomalies.
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
Analytical survey of active intrusion detection techniques in mobile ad hoc n...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
The spread of information networks in communities and organizations have led to a daily huge volume of information exchange between different networks which, of course, has resulted in new threats to the national organizations. It can be said that information security has become today one of the most challenging areas. In other words, defects and disadvantages of computer network security address irreparable damage for enterprises. Therefore, identification of security threats and ways of dealing with them is essential. But the question raised in this regard is that what are the strategies and policies to deal with security threats that must be taken to ensure the security of computer networks? In this context, the present study intends to do a review of the literature by using earlier researches and library approach, to provide security solutions in the face of threats to their computer networks. The results of this research can lead to more understanding of security threats and ways to deal with them and help to implement a secure information platform.
An Assessment of Intrusion Detection System IDS and Data Set Overview A Compr...ijtsrd
Millions of people worldwide have Internet access today. Intrusion detection technology is a modern wave of information technology monitoring devices to deter malicious activities. Malware development malicious software is a vital problem when it comes to designing intrusion detection systems IDS . The key challenge is to recognize unknown and hidden malware, because malware writers use various evasion techniques to mask information to avoid IDS detection. Malicious attacks have become more sophisticated and Furthermore, threats to security have increased, including a zero day attack on internet users. Through the use of IT in our daily lives, computer security has become critical. Cyber threats are becoming more complex and pose growing challenges when it comes to successful intrusion detection. Failure to prevent invading information, such as data privacy, integrity and availability can undermine the credibility of security services. Specific intrusion detection approaches were proposed in the literature to combat computer security threats. This paper consists of a literature survey of the IDS that uses program algorithms to use specific data collection and forensic techniques in real time. Data mining techniques for cyber research are introduced in support of intrusion detection. Mohammed I. Alghamdi "An Assessment of Intrusion Detection System (IDS) and Data-Set Overview: A Comprehensive Review of Recent Works" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd35730.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-security/35730/an-assessment-of-intrusion-detection-system-ids-and-dataset-overview-a-comprehensive-review-of-recent-works/mohammed-i-alghamdi
Comparative Study on Intrusion Detection Systems for Smartphonesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
Analytical survey of active intrusion detection techniques in mobile ad hoc n...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
The spread of information networks in communities and organizations have led to a daily huge volume of information exchange between different networks which, of course, has resulted in new threats to the national organizations. It can be said that information security has become today one of the most challenging areas. In other words, defects and disadvantages of computer network security address irreparable damage for enterprises. Therefore, identification of security threats and ways of dealing with them is essential. But the question raised in this regard is that what are the strategies and policies to deal with security threats that must be taken to ensure the security of computer networks? In this context, the present study intends to do a review of the literature by using earlier researches and library approach, to provide security solutions in the face of threats to their computer networks. The results of this research can lead to more understanding of security threats and ways to deal with them and help to implement a secure information platform.
An Assessment of Intrusion Detection System IDS and Data Set Overview A Compr...ijtsrd
Millions of people worldwide have Internet access today. Intrusion detection technology is a modern wave of information technology monitoring devices to deter malicious activities. Malware development malicious software is a vital problem when it comes to designing intrusion detection systems IDS . The key challenge is to recognize unknown and hidden malware, because malware writers use various evasion techniques to mask information to avoid IDS detection. Malicious attacks have become more sophisticated and Furthermore, threats to security have increased, including a zero day attack on internet users. Through the use of IT in our daily lives, computer security has become critical. Cyber threats are becoming more complex and pose growing challenges when it comes to successful intrusion detection. Failure to prevent invading information, such as data privacy, integrity and availability can undermine the credibility of security services. Specific intrusion detection approaches were proposed in the literature to combat computer security threats. This paper consists of a literature survey of the IDS that uses program algorithms to use specific data collection and forensic techniques in real time. Data mining techniques for cyber research are introduced in support of intrusion detection. Mohammed I. Alghamdi "An Assessment of Intrusion Detection System (IDS) and Data-Set Overview: A Comprehensive Review of Recent Works" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd35730.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-security/35730/an-assessment-of-intrusion-detection-system-ids-and-dataset-overview-a-comprehensive-review-of-recent-works/mohammed-i-alghamdi
Comparative Study on Intrusion Detection Systems for Smartphonesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Collaborative Intrusion Detection System for Cloud Computingijsrd.com
Cloud computing is a computing paradigm that shifts drastically from traditional computing architecture. Although this new computing paradigm brings many advantages like utility computing model but the design in not flawless and hence suffers from not only many known computer vulnerabilities but also introduces unique information confidentiality, integrity and availability risks as well due its inherent design paradigm. To provide secure and reliable services in cloud computing environment is an important issue. To counter a variety of attacks, especially large-scale coordinated attacks, a framework of Collaborative Intrusion Detection System (IDS) is proposed. The proposed system could reduce the impact of these kinds of attacks through providing timely notifications about new intrusions to Cloud users' systems. To provide such ability, IDSs in the cloud computing regions both correlate alerts from multiple elementary detectors and exchange knowledge of interconnected Clouds with each other.
1. Cyber Ethics and Cyber Crime
2. Security in Social Media & Risk of Child Internet
3. Social media in Schools and photo privacy
4. Risk of OSNs and Security, Privacy of Facebook
5. Risk and Security of Social Networking site Facebook and Twitter
6. Risk analysis of Government and Online Transaction
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.
“AI techniques in cyber-security applications”. Flammini lnu susec19Francesco Flammini
▪ “AI techniques in cyber-security applications”. Invited speech at “Sunetdagarna våren 2019” (conference of the association of Swedish universities), April 1-4 2019, Växjö, Sweden.
Mobile Ad hoc Networks (MANETs) are wireless networks consisted of mobile free nodes that can move anywhere at any time without the need to any fixed infrastructure or any centralized administration. In this category of networks existing nodes must rely on each other to play the role of routers or switches instead of using central ones. The self-organized nature of such environments made MANETs vulnerable against many security threats. As a result, providing security requirements in MANETs is one of the most interesting challenges in such a network. In this group of networks, the use of cryptographic solutions is one of the most interesting security issues. The importance of this scientific area in MANETs is more drastic by considering that mentioned schemes must be lightweight enough to be appropriate for resource constrained platforms in such environment. This paper has tried to represent the position of cryptographic issues in MANETs. Moreover, security issues in mobile Ad hoc networks beside of different classes of public key cryptosystems have been introduced.
Network infrastructures have played important part in most daily communications for business industries,
social networking, government sectors and etc. Despites the advantages that came from such
functionalities, security threats have become a daily struggle. One major security threat is hacking.
Consequently, security experts and researchers have suggested possible security solutions such as
Firewalls, Intrusion Detection Systems (IDS), Intrusion Detection and Prevention Systems (IDP) and
Honeynet. Yet, none of these solutions have proven their ability to completely address hacking. The reason
behind that, there is a few researches that examine the behavior of hackers. This paper formally and
practically examines in details the behavior of hackers and their targeted environments. Moreover, this
paper formally examines the properties of one essential pre-hacking step called scanning and highlights its
importance in developing hacking strategies. Also, it illustrates the properties of hacking that is common in
most hacking strategies to assist security experts and researchers towards minimizing the risk of hack.
Intrusion Detection System (IDS) is meant to be a software application which monitors the network or system activities and finds if any malicious operations occur. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. Nowadays, hackers use different types of attacks for getting the valuable information. Many intrusion detection techniques, methods and algorithms help to detect these attacks. This main objective of this paper
is to provide a complete study about the definition of intrusion detection, history, life cycle, types of intrusion detection methods, types of attacks, different tools and techniques, research needs, challenges and
applications.
Intelligent Network Surveillance Technology for APT Attack DetectionsAM Publications,India
Recently, long-term, advanced cyber-attacks targeting a specific enterprise or organization have been occurring again. These attacks occur over a long period and bypass detection by security systems unlike the existing attack pattern. For such reason, they create problems such as delayed real-time response and detection after damages have already been incurred. This paper introduces the design of technology that applies real-time network traffic monitoring to detect unknown functional cyber-attack on the network. Specifically, the algorithm was verified and evaluated in terms of performance in an actual commercial environment. Cyber-attack detection performance is expected to be improved by enhancing the algorithm and processing large volumes of traffic
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 BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL ...IJNSA Journal
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal
activities happening in a computer system. In recent years different soft computing based techniques have
been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect
technology. This has provided an opportunity for data mining to make quite a lot of important
contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique
by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The
proposed technique has five major steps in which, first step is to perform the relevance analysis, and then
input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each
of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.
Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF-
SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset
and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our
technique could achieve better accuracy for all types of intrusions. The results of proposed technique are
compared with the other existing techniques. These comparisons proved the effectiveness of our
technique.
A Collaborative Intrusion Detection System for Cloud Computingijsrd.com
Cloud computing is a computing paradigm that shifts drastically from traditional computing architecture. Although this new computing paradigm brings many advantages like utility computing model but the design in not flawless and hence suffers from not only many known computer vulnerabilities but also introduces unique information confidentiality, integrity and availability risks as well due its inherent design paradigm. To provide secure and reliable services in cloud computing environment is an important issue. To counter a variety of attacks, especially large-scale coordinated attacks, a framework of Collaborative Intrusion Detection System (IDS) is proposed. The proposed system could reduce the impact of these kinds of attacks through providing timely notifications about new intrusions to Cloud users' systems. To provide such ability, IDSs in the cloud computing regions both correlate alerts from multiple elementary detectors and exchange knowledge of interconnected Clouds with each other.
1. Cyber Ethics and Cyber Crime
2. Security in Social Media & Risk of Child Internet
3. Social media in Schools and photo privacy
4. Risk of OSNs and Security, Privacy of Facebook
5. Risk and Security of Social Networking site Facebook and Twitter
6. Risk analysis of Government and Online Transaction
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.
“AI techniques in cyber-security applications”. Flammini lnu susec19Francesco Flammini
▪ “AI techniques in cyber-security applications”. Invited speech at “Sunetdagarna våren 2019” (conference of the association of Swedish universities), April 1-4 2019, Växjö, Sweden.
Mobile Ad hoc Networks (MANETs) are wireless networks consisted of mobile free nodes that can move anywhere at any time without the need to any fixed infrastructure or any centralized administration. In this category of networks existing nodes must rely on each other to play the role of routers or switches instead of using central ones. The self-organized nature of such environments made MANETs vulnerable against many security threats. As a result, providing security requirements in MANETs is one of the most interesting challenges in such a network. In this group of networks, the use of cryptographic solutions is one of the most interesting security issues. The importance of this scientific area in MANETs is more drastic by considering that mentioned schemes must be lightweight enough to be appropriate for resource constrained platforms in such environment. This paper has tried to represent the position of cryptographic issues in MANETs. Moreover, security issues in mobile Ad hoc networks beside of different classes of public key cryptosystems have been introduced.
Network infrastructures have played important part in most daily communications for business industries,
social networking, government sectors and etc. Despites the advantages that came from such
functionalities, security threats have become a daily struggle. One major security threat is hacking.
Consequently, security experts and researchers have suggested possible security solutions such as
Firewalls, Intrusion Detection Systems (IDS), Intrusion Detection and Prevention Systems (IDP) and
Honeynet. Yet, none of these solutions have proven their ability to completely address hacking. The reason
behind that, there is a few researches that examine the behavior of hackers. This paper formally and
practically examines in details the behavior of hackers and their targeted environments. Moreover, this
paper formally examines the properties of one essential pre-hacking step called scanning and highlights its
importance in developing hacking strategies. Also, it illustrates the properties of hacking that is common in
most hacking strategies to assist security experts and researchers towards minimizing the risk of hack.
Intrusion Detection System (IDS) is meant to be a software application which monitors the network or system activities and finds if any malicious operations occur. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. Nowadays, hackers use different types of attacks for getting the valuable information. Many intrusion detection techniques, methods and algorithms help to detect these attacks. This main objective of this paper
is to provide a complete study about the definition of intrusion detection, history, life cycle, types of intrusion detection methods, types of attacks, different tools and techniques, research needs, challenges and
applications.
Intelligent Network Surveillance Technology for APT Attack DetectionsAM Publications,India
Recently, long-term, advanced cyber-attacks targeting a specific enterprise or organization have been occurring again. These attacks occur over a long period and bypass detection by security systems unlike the existing attack pattern. For such reason, they create problems such as delayed real-time response and detection after damages have already been incurred. This paper introduces the design of technology that applies real-time network traffic monitoring to detect unknown functional cyber-attack on the network. Specifically, the algorithm was verified and evaluated in terms of performance in an actual commercial environment. Cyber-attack detection performance is expected to be improved by enhancing the algorithm and processing large volumes of traffic
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 BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL ...IJNSA Journal
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal
activities happening in a computer system. In recent years different soft computing based techniques have
been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect
technology. This has provided an opportunity for data mining to make quite a lot of important
contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique
by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The
proposed technique has five major steps in which, first step is to perform the relevance analysis, and then
input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each
of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.
Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF-
SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset
and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our
technique could achieve better accuracy for all types of intrusions. The results of proposed technique are
compared with the other existing techniques. These comparisons proved the effectiveness of our
technique.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
Data Mining Techniques for Providing Network Security through Intrusion Detec...IJAAS Team
Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Intrusion Detection System (IDS) Development Using Tree-Based Machine Learnin...IJCNCJournal
The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features.
Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning...IJCNCJournal
The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features.
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.
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.
Review of Intrusion and Anomaly Detection Techniques IJMER
Intrusion detection is the act of detecting actions that attempt to compromise the
confidentiality, integrity or availability of a resource. With the tremendous growth of network-based
services and sensitive information on networks, network security is getting more and more importance
than ever. Intrusion poses a serious security threat in a huge network environment. The increasing use of
internet has dramatically added to the growing number of threats that inhabit within it. Intrusion
detection does not, in general, include prevention of intrusions. Now a days Network intrusion detection
systems have become a standard component in the area of security infrastructure. This review paper tries
to discusses various techniques which are already being used for intrusion detection.
New Hybrid Intrusion Detection System Based On Data Mining Technique to Enhan...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
New Hybrid Intrusion Detection System Based On Data Mining Technique to Enhan...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An approach for ids by combining svm and ant colony algorithmeSAT Journals
Abstract This piece of work researches the intrusion detection problem of the network sanctuary; the primary task is to classify network behavior as normal or abnormal while reducing misclassification. In this paper, two efficient data mining algorithms are combined together to detect the network intrusion. Combining SVM and Ant colony (CSVAC) used for well-organized data classification, this technique takes the advantage of both the algorithm while avoiding their weaknesses. This algorithm is implemented and evaluated using standard benchmark KDDCUP99 data set. Experimental results drastically well produce superior results than the other algorithm in terms of accuracy rate and run time efficiency, and this algorithm able to detect the new types of attacks Keywords: Intrusion Detection; Support Vector Machine; Ant colony; Combined Support vector with ant colony
Similar to Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection Approach (20)
Novel Methodology of Data Management in Ad Hoc Network Formulated using Nanos...Drjabez
In Ad hoc Network of Nanosensors for Wastage detection, clustering assist in nodal communication and in organization of the data fetched by the nanosensors in the network. The attempt of traditional cluster formation techniques degraded the formation of cluster in a precise manner. The data from the nanosensors which act as the nodes of the network have to be distinctively added into the clusters. The dynamic path selection cluster would achieve this distinct addition by dynamically creating a path to the data as an initial process and then redirecting the data to their appropriate cluster based to the readied scheme.
Profile Analysis of Users in Data Analytics DomainDrjabez
Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. 339J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
1. Introduction
With the high usage of Internet in our day today life, security of network has become the key foundation to
all web applications, like online auctions, online retail sales, etc. Detection of Intrusion, attempts to detect the
attacks of computer by examining different information records observed in network processes [2] [9]. This can be
considered as one of the significant ways to effectively deal with the problems in network security.
An intrusion in the internet can compromise the data security through several internet means. Nowadays,
the fast rising networks proliferation, data transfer rate, and an unpredictable Internet usage have added more
anomaly problems. Thus researchers need to develop more reliable, effective, and self-monitoring systems, which
sort troubles and can carry out operation devoid of human interaction. By undergoing this kind of attempts,
catastrophic failures of susceptible systems can be reduced.
Detection stability and detection precision are two key indicators used to evaluate IDS (Intrusion Detection System)
[26]. Many of the IDS research studies have been done in order to improve the detection stability and detection
precision [22]. In the beginning stage, the research work focus lies in using statistical approaches and rule-based
expert systems [17]. But, the results of statistical approaches and rule-based expert systems were not accurate, when
encountering larger datasets. In order to overcome the abovementioned problem, many data mining techniques were
developed [7] .
Some machine-learning paradigms containing Linear Genetic Programming (LGP) [19], neural networks [18],
Bayesian networks, Support Vector Machines (SVM), Fuzzy Inference Systems (FISs) [25], Multivariate Adaptive
Regression Splines (MARS) [20] etc., have been investigated for the design of Intrusion Detection System (IDS).
Thus, one of the most common techniques in machine-learning paradigms is known as Neural Network (NN) that
should be used for resolving a lot of complex practical problems which has been successfully applied into Intrusion
Detection System [9]. Nevertheless, the major drawbacks of Neural Network-based IDS exist in two features:
1. Lower Detection Precision- particularly for low-frequent attacks, e.g., U2R (User to Root), R2L (Remote
to Local).
2. Weaker detection stability [4].
To solve the above two problems, this research work propose a novel approach for outlier computation-
based IDS, Outlier Detection Approach, to enhance the detection precision for low-frequent attacks and
detection stability. The proposed approach has got two stages such as training with normal big datasets and
testing with intrusion datasets. A set of various big datasets are used to train our IDS in the initial stage at
distributed storage environment. Normal big datasets are improving the performance of Intrusion Detection
System. Assume an intrusion dataset which is used to compute an error value with trained big data sets. If
number of error value is increased such as the specified threshold then the tested data set consider as
anomaly dataset.
The rest of the paper is organized as follows: Section II explains the existing work. Next, Section III
provides the details of the concept and classification of normal intrusion detection system components and
its proposed approach. Section IV shows the proposed approach, the experimental results and its analysis.
Finally, Section V concludes the work and its future directions.
2. Literature Survey
This section deals with the attempts made by researcher in the area of network based intrusion detection system and
most of the detection works were based on KDD dataset. An expert system based on rules and statistical approaches
are the two commonly used approaches to ensure intrusion detection. The Expert system based on rules will detect
the known intrusion in high rate and it will not identify new intrusion. Where, the database should be continuously
updated. In statistical approach, Intrusion Detection System includes different methods like Cluster analysis,
Multivariate analysis, Bayesian analysis, and Principal component analysis. Many new techniques from data mining
should be proposed to overcome the problems of above mentioned approaches. Many results are produced in the
KDD cup 99 dataset research work and they are briefly discussed.
Anderson [25] suggested an intrusion detection method to efficiently detect the intrusion. An Intrusion
Detection Mechanism using Time- series, Markov chains, and statistics was developed by Denning [3] Denning
considered that the changes in the normal behavior of user are treated as anomalous. For monitoring and detecting
3. 340 J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
user’s events an Expert System of intrusion detection was developed by Stanford Research Centre. This centre also
developed next generation mechanism which includes audit profiles of user’s and can monitor the current status of
the user, if any change occurs with user’s activity compared with audit profile of user then it will generate am alarm.
Haystack [22] later developed a framework to estimate an intrusion detection method based on user and anomaly
strategies. Six types of intrusion were detected and those includes the masquerade attacks, malicious use, leakage,
service denial, unauthorized user’s break- ins attempt, and access control of security system. The source fire
developed indicates a network based intrusion detection and prevention mechanism called SNORT system which is
an open source. Forrest [10] in 1996 created a normal profile based on analyzing the call sequences between
intrusion detection and protection against human system. An attack in this system is considered as the sequence
deviation from normal profile sequence. Thus, this system works offline using previously collected information and
implements view table algorithm for learning program profiles significantly.
Duan et al. [8] have concentrated on identifying compromised machines that are recruited to detect spam
zombies. An approach SPOT is proposed to scan sequentially outgoing messages by implementing SPRT
(Sequential Probability Ratio Test). This method quickly estimates whether a host is compromised or not.
Identifying compromised machines using malware infection system is stated by Bot hunter [13]. This system has
large no of steps that allow intrusion detection alarms correlation triggered using inbound traffic with outgoing
message exchange pattern results. Bot Sniffer [14] explained in his work about compromised machine
characteristics which are a uniform temporal-spatial behavior for detecting zombies. This method identifies zombies
by combining flows based on server connections and searching flows with similar behavior respectively.
Kumar and Goyal [12] have explained implements genetic algorithms in dataset training to classify the
labels that are smurf attacked and achieves low false positive ratio of 0.2%. Further work done by Abdullah [1] and
co-workers elaborated intrusion detection classification rules using genetic algorithms. Intrusion detection rules
using genetic algorithms was also the study made by Ojugo et al. [21]. This method uses fitness function for
estimating the rules.
Machine learning techniques are also implemented to detect the intrusion. Existing machine learning
techniques (Artificial Neural Networks - ANN) for intrusion detection was described by Roshani team [23].
Gaikwad et al [11] introduced a technique based on fuzzy clustering and ANN approach. This method
could be applicable to overcome the issues of weak stability detection as well as low precision detection. The restore
point in this method was employed for registry keys, system files roll back, project database and installed programs.
Fuzzy clustering will generate different subsets for training in order to reduce the amount of subset size and
complexity. Then each subset is trained with different type of artificial neural network and finally processed to
obtain significant results. Jaiganesh et al [15] suggested a novel back propagation model for intrusion detection. This
method makes training pair with a combination of input and equivalent target were generated and implemented into
the network. Performance success can be measured by false alarm and detection rate. Detection rate was proven to
be less than 80% for U2R, R2L, DoS and Probe attacks. However, the major issue of the method was found to be
much inefficient to detect hidden attackers present in the system. Devikrishna et al [5] used MLP (Multi Layer
Perceptron) architecture for intrusion detection that detects and classifies attacks into six types. MLP method was
considered as a failure model due to irrelevant output .In the present paper we have tried to overcome this query and
to establish a better detection technique .
Lin GU et al [16] proposed empirical study for right choice of unstable growing demand in processing big
data which entails huge burden of storage, data center communication and computation which brings substantial
operational expenditure for data providing centers. Apart from traditional cloud service, an important characteristic
of big data was found to be the tight coupling of computation and data computation tasks were performed only with
relevant data. But the means to improve the IDS is not clearly conveyed so far by any of the researchers. Thus, the
main aim of this paper is to implement a clear picture of the IDS using distributed big data concept.
Issues of existing techniques
Many issues are been stated in the existing literature survival like additional training time, accurate
identification of low common attacks and attacks classification. In order to solve the issue of additional training
time, it is must to develop a new high-speed algorithm for intrusion detection system and its results will be tested
with existing techniques. In contrast to the existing approaches that performed some kind of inefficiency in intrusion
detection, the main aim of our research work is to propose a new high speed algorithm for reducing training time.
The obtained results are also to be discussed along with the existing methods.
4. 341J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
3. Intrusion Detection System
3.1 Classification of Intrusion Detection Systems (IDS)
Classification is one of the best – known solution approaches. National Institute of Standards and
Technology (NIST) organization provides guidance document on Intrusion Detection Systems [24].
Intrusion Detection System briefly classified into three different categories:
Host-based IDS
Network-based IDS
Vulnerability-assessment IDS
There are two basic models used to analyze the events and discover attacks:
Misuse detection model – Intrusion Detection System detect intrusions by looking for similar activities
such as vulnerabilities or known intrusion signatures.
Anomaly detection model - IDS detect intrusions by searching « abnormal » network traffic.
The misuse detection model is commonly referred as IDS commercial tool; always Vendors must update intrusion
signatures. Anomaly detection based IDS model have the capability to detect attack symptoms without specifying
attack models, but these models are very sensitive to false alarms. In the present study we have utilized the proposed
IDS approach’s based on the anomaly detection model.
3.2 System Architecture
Fig. 1: Proposed System Architecture
Our main aim is to develop an IDS based on anomaly detection model that would be precise, not easily cheated by
small variations in patterns, low in false alarms, adaptive and be of real time. The Figure 1 describes the proposed
system architecture were the intrusion packets are received from the internet then SNORT is used to collect the
datasets. Initially, the features extracted from data packets then it forwarded to our proposed IDS. Then, proposed
IDS compute the distance between the extracted features and trained model. Here, trained model consists of big
datasets with distributed storage environment to improve the performance of Intrusion Detection system. Thus, the
outlier value is greater than the specified threshold then it generates the false alarm.
Intrusion Detection System
Feature Extraction
Internet Packet
Trained Model (Big Dataset)
Anomaly Detection
(NOF Outlier Detection Approach)
Generate False Alarm
Trained M
Distributed storage Area
5. 342 J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
3.2 Pseudocode: Outlier Detection Approach
The normal data objects have a dense neighborhood whereas “outliers” are far apart from their neighbors.
The outliers are the objects of the outer layers. The major idea behind this approach is to assign a data example to
being outlier degree called Neighborhood Outlier Factor (NOF) and to find the rare data whose behavior is very
exceptional when compared with large amount of normal data. The algorithm steps used to calculate NOFs for all
data examples are as follows:
1. For each data example O, calculate the k-distance is the nearest neighborhood (where all points in a k-distance
forms sphere).
2. Next, calculate the reachability distance for every data example O with respect to data example p as: reach-
distance (O,p) = max{k-distance (p), d(O,p)}, where d(O,p) is the distance between data example O and data
example p.
3.Then, calculate local reachability density for each data example O , inverse of the average reachability distance
is based on the MinPts (minimum number of objects) data example O and its nearest neighbors.
4. Calculate NOF to all data example O as an average of the data example O’s local reachability density ratios and
local reachability density of O’s MinPts nearest neighbors.
The benefits of proposed NOF approach is illustrated in Figure 2. Clusters are formally defined as maximal sets of
density-connected objects. Here a simple two-dimensional dataset is taken with much larger number of examples in
cluster C1 then C2. So the cluster density of C2 is extensively higher than that of C1 cluster density. For each
example consider an object q inside the cluster C1, the distance between the example q and its nearest neighbor is
greater than the distance between the example p2 and the nearest neighbor from the cluster C2, and the example p2
will not be considered as outlier.
Fig. 2: NOF Outlier Detection Approach
Consequently, the outlier detection lies in the field of statistics. Nevertheless, the example p1 can be
detected as outlier using only the nearest neighbor distances. Alternatively, NOF is capable to capture both (p1 and
p2) outliers due to the fact that it considers the density all round the points.
4. Results and Discussion
4.1 Experimental Setup
In our study, a dataset is extracted and number of experiments were based on the extracted dataset in order
to measure the IDS performance. Experiments were carried out based on the following configuration: Windows 7,
Intel Pentium (R), CPU G2020 and processer speed 2.90 GHz respectively.
The extracted data set includes training data of about two thousand connection records and test data
includes five thousand connection records. In addition, dataset includes a group of forty one derived features
6. 343J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
received from every connection and also a group of labels that identifies the connection record status whether it is a
normal type or attacked type.
4.2 Anomaly Results
Table 1: Fragmentation of Attributes from the IP datasets
List of partial attributes names obtained from network datasets are as shown in Table 1. These attributes
detects whether the received network dataset is anomaly or not.
Table 2: Fragmentation of trained normal big-data set model
ID Duration Flag Source
byte
Destination
byte
ID Duration Flag Source
byte
Destination
byte
1. 81 18 522 0 6. 66 28 522 0
2. 12 61 0 0 7. 78 132 18 0
3. 22 61 0 0 8. 45 134 0 0
4. 65 184 520 0 9. 74 58 89 0
5. 45 47 0 0 10. 35 1 50 0
Dataset’s are obtained from different communication level network with different internet service
provider’s policy. Internet service provider policy will vary for different communication levels (Table 2).
Table 3: Fragmentation of information Received from various user
The network information of user’s will vary because different user’s use different internet service
providers. (Table 3)
S. No Attributes S.No Attributes
1 Duration 6 Destination bytes
2 Protocol type 7 Number failed logins
3 Service 8 Service received error rate
4 Flag 9 Different service rate
5 Source bytes 10 Destination host count
ID Duration Flag Source
byte
Destination
byte
ID Duration Flag Source
byte
Destination
byte
1. 10 SF 491 0 6. 98 233 616 0
2. 22 334 0 0 7. 569 147 105 0
3. 56 146 0 0 8. 45 RSTR 0 0
4. 78 199 420 0 9. 87 255 861 0
5. 66 28 0 0 10. 35 1 0 0
7. 344 J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
Table 4: Distance and Outlier value of tested data
ID Distance Outlier
value
ID Distance Outlier
value
1. 2.5 5 6. 1.2 2
2. 4.6 8 7. 2.7 3
3. 3.6 7 8. 4.2 4
4. 5.6 10 9. 2.7 3
5. 2.4 4 10. 2.9 5
The distance and outlier values of tested data which is calculated by proposed outlier detection method. It
indicates that outlier values increase if distance between the normal and tested dataset increases. The results are
shown in Table 4.
4.3 Discussion
4.3.1 Comparison of proposed approach and Existing approach (Execution Time Vs Dataset Size)
Fig 3: Big-Dataset size Vs Execution Time
Figure 3 shows the overview of various execution times with various size of dataset. The proposed Intrusion
Detection System takes less execution time at every level rather than other existing machine learning approaches.
The cause is less trained datasets thus the distance computation is easy between the trained and testing dataset
respectively
4.3.2 Comparison of proposed approach and Existing approach (Anomaly Detection Rate Vs Dataset Size)
Fig 4: Big-Dataset size Vs Anomaly Detection
Fig.4 shows the anomaly detection rate in the computer network. The proposed Intrusion Detection System
identifies almost all type of attacks such as Probe, DoS, U2R and R2L. The anomaly detection rate depends on the
outlier values testing data. If the outlier value increase then the dataset assumed acts as intrusion dataset.
8. 345J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
4.3.3 Comparison of proposed approach and Existing approach (CPU Utilization Vs Dataset Size)
Fig 5: Big-Dataset size Vs CPU Utilization
The figure 5 shows the graphical comparison of CPU utilization levels with various sizes of datasets. In the
machine learning approaches’, CPU utilization is very high when compared with proposed approach. Most of the
research papers have assigned machine learning approaches only with the help of huge quantity of training datasets
and training functions. In our proposed approach we are using only limited datasets to train the proposed IDS.
5. Conclusion
In this paper, we have presented the details of a new approach called Outlier Detection approach to detect
the intrusion in the computer network. Our training model consists of big datasets with distributed environment that
improves the performance of Intrusion detection system. The proposed approach is also been tested with the KDD
datasets that are received from real world. The machine learning approaches detect the intrusion in the computer
network with huge execution time and storage to predict the when compared to the proposed IDS system which
takes less execution time and storage to test the dataset .Here in this study, the performance of proposed IDS is
better than that of other existing machine learning approaches and can significantly detect almost all anomaly data in
the computer network. In future, the proposed work can be possibly used for various distance computation function
between the trained model and testing data. Our research work can be considered to improve the efficiency of IDS
in a better manner.
References:
1. Abdullah, B., Abd-algafar I., Salama G. I. and Abd-alhafez A. Performance Evaluation of a Genetic Algorithm Based
Approach to Network Intrusion Detection System, Proceedings of 13th International Conference on Aerospace Sciences and
Aviation Technology (ASAT-13), Military Technical College, Cairo, Egypt, 2009;1-5.
2. Anderson, J. P. Computer security threat monitoring and surveillance. Technical Report, Fort Washington, PA, USA.,1980;9-
11.
3. Anderson, D., Frivold, T. and Valdes, A. Next-generation intrusion detection expert system (NIDES): A summary Technical
Report SRI–CSL–95–07,Computer Science Laboratory,SRI International, May 1995.
4. Beghdad, R. Critical study of neural networks in detecting intrusions. Computers and Security, 27(5-6): 2008;168–175.
5. Devikrishna, K. S. and Ramakrishna , B. B. .An Artificial Neural Network based Intrusion Detection System and
Classification of Attacks", International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Jul-Aug
2013, 3(4): 1959-1964.
6. Denning, D. E.. An intrusion detection model, IEEE Transactions on Software Engineering, CA,. IEEE Computer Society
Press;1987.
9. 346 J. Jabez and B. Muthukumar / Procedia Computer Science 48 (2015) 338 – 346
7. Dokas, P., Ertoz, L., Lazarevic, A., Srivastava, J.and Tan, P. N. Data mining for network intrusion detection. Proceeding of
NGDM, 2002;21–30.
8. Duan, Z., Chen, P., Sanchez, F., Dong, Y., Stephenson, M. and J. M. Barker, M. (2012). Detecting spam zombies by
monitoring outgoing messages, IEEE Trans. Dependable and Secure Computing, Apr 2012; 9(2):198–210
9. Endorf, C., Schultz, E. and Mellander, J. (2004). Intrusion detection and prevention. California: Mc Graw-Hill.
10. Forrest, S., Hofmeyr, S. A. , Somayaji, A. and Longstaff, T. A. A Sense of Self for Unix Processes, IEEE Symposium on
Research in Security and Privacy, Oakland, CA, USA, 1996;120--128.
11. Gaikwad, Sonali Jagtap, D.P. Kunal Thakare and Vaishali Budhawant. Anomaly Based Intrusion Detection System Using
Artificial Neural Network and fuzzy clustering., International Journal of Engineering Research & Technology (IJERT), ISSN:
2278-0181, November- 2012; 1(9).
12. Goyal, A. and Kumar, C. .GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System, Electrical
Engineering and Computer Science, North West University, Technical Report;2008.
13. Gu, G., Porras, P., Yegneswaran V., Fong, M. and Lee, W. BotHunter: detecting malware infection through IDS-driven
ialog correlation, Proc. of 16th USENIX Security Symp. (SS ’07), Aug. 2007; 12:1–12:16.
14. Gu, G., Zhang, J. and Lee, W. (2008). BotSniffer: detecting botnet command and control channels in network traffic, Proc. of
15th
Ann. Network and Distributed Sytem Security Symp. (NDSS ’08), Feb. 2008.
15. Jaiganesh, V., Sumathi, P. and Mangayarkarasi, S. ,An Analysis of Intrusion Detection System using back propagation
neural network, IEEE Computer Society Publication;2013.
16. Lin Gu, Deze Zeng, Peng Li, and Song Guo. Cost Minimization for Big Data Processing in Geo-Distributed Data Centers,
IEEE Transactions on Emerging Topics in Computing;2014.
17. Manikopoulos, C.and Papavassiliou, S. Network intrusion and fault detection: A statistical anomaly approach. IEEE
Communications Magazine, 40(10);2002 76–82.
18. Mukkamala, S., Sung, A.H., Abraham, A. Intrusion detection using ensemble of soft computing paradigms, third
international conference on intelligent systems design and applications, intelligent systems designand applications, advances in
soft computing. Germany: Springer,2003; 239–248.
19. Mukkamala, S., Sung, A.H., Abraham, A. Modeling intrusion detection systems using linear genetic programming approach,
The 17th
international conference on industrial & engineering applications of artificial intelligence and expert systems,
innovations in applied artificial intelligence. In: Robert O., Chunsheng Y., Moonis A., editors. Lecture Notes in Computer
Science, Germany: Springer; 2004a. 3029: 2004;633–642.
20. Mukkamala, S., Sung, A.H., Abraham, A. and Ramos ,V. Intrusion detection systems using adaptive regression splines. In:
Seruca I, Filipe J, Hammoudi S, Cordeiro J, editors. Proceedings of the 6th international conference on enterprise information
systems, ICEIS’, Portugal. 2004b. 3: 2004;26–33.
21. Ojugo, A. A., Eboka, A. O., Okanta, O. E., Yora, R. E. and Aghware, F. O.Genetic Algorithm Rule-Based Intrusion
Detection System (GAIDS), Journal of Emerging Trends in Computing and Information Sciences, 3(8);2012; 1182 – 1194.
22. Patcha, A. and Park, J. M. An overview of anomaly detection techniques: Existing solutions and latest technological trends.
Computer Networks, 51(12);2007; 3448–3470.
23. Roshani Gaidhane, Vaidya, C. and Raghuwanshi, M. Survey.Learning Techniques for Intrusion Detection System (IDS),
International Journal of Advance Foundation and Research in Computer (IJAFRC) Feb 2014. ISSN 2348 – 4853, 2014;1(2).
24. Planquart, J.P. (2001). Article paper ,Application of Neural Networks to Intrusion Detection, SANS Institute .1-3.
25. Shah, K., Dave, N., Chavan, S., Mukherjee, S., Abraham, A. and Sanyal S. Adaptive neuro-fuzzy intrusion detection system.
IEEE International Conference on Information Technology: Coding and Computing (ITCC’04), vol. 1. USA: IEEE Computer
Society;2004; 70–74.
26. Silva, L. D. S., Santos, A. C., Mancilha, T. D., Silva, J. D. and Montes, A. Detecting attack signatures in the real network
traffic with ANNIDA. Expert Systems with Applications, 34(4);2008; 2326–2333.