The document discusses various ways that data mining can be applied for security applications such as intrusion detection, firewall policy management, worm detection, and counter-terrorism surveillance. It describes techniques like anomaly detection, link analysis, classification, and prediction that can help detect cyber attacks, trace malware authors, and predict future threats. It also addresses challenges of working with real-time streaming data from sensors for critical applications.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
What is IDS?
Software or hardware device
Monitors network or hosts for:
Malware (viruses, trojans, worms)
Network attacks via vulnerable ports
Host based attacks, e.g. privilege escalation
What is in an IDS?
An IDS normally consists of:
Various sensors based within the network or on hosts
These are responsible for generating the security events
A central engine
This correlates the events and uses heuristic techniques and rules to create alerts
A console
To enable an administrator to monitor the alerts and configure/tune the sensors
Different types of IDS
Network IDS (NIDS)
Examines all network traffic that passes the NIC that the sensor is running on
Host based IDS (HIDS)
An agent on the host that monitors host activities and log files
Stack-Based IDS
An agent on the host that monitors all of the packets that leave or enter the host
Can monitor a specific protocol(s) (e.g. HTTP for webserver)
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Crime Data Analysis, Visualization and Prediction using Data MiningAnavadya Shibu
This paper presents a general idea about the
model of Data Mining techniques and diverse crimes. It also
provides an inclusive survey of competent and valuable
techniques on data mining for crime data analysis. The
objective of the data mining is to recognize patterns in
criminal manners in order to predict crime anticipate
criminal activity and prevent it. This project implements a
novel data mining techniques like KNN, Text Clustering, IR
tree for investigating the crime data sets and sorts out the
accessible problems. The collective knowledge of various
data mining algorithms tend certainly to afford an
enhanced, incorporated, and precise result over the crime
prediction in the banking sectors Our law enforcement
organizations require to be adequately outfitted to defeat
and prevent the crime. This project is developed using Java
as front-end and MySQL as back-end. Supporting
applications like Sunset, NetBeans are used to make the
portal more interactive.
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.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
What is IDS?
Software or hardware device
Monitors network or hosts for:
Malware (viruses, trojans, worms)
Network attacks via vulnerable ports
Host based attacks, e.g. privilege escalation
What is in an IDS?
An IDS normally consists of:
Various sensors based within the network or on hosts
These are responsible for generating the security events
A central engine
This correlates the events and uses heuristic techniques and rules to create alerts
A console
To enable an administrator to monitor the alerts and configure/tune the sensors
Different types of IDS
Network IDS (NIDS)
Examines all network traffic that passes the NIC that the sensor is running on
Host based IDS (HIDS)
An agent on the host that monitors host activities and log files
Stack-Based IDS
An agent on the host that monitors all of the packets that leave or enter the host
Can monitor a specific protocol(s) (e.g. HTTP for webserver)
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Crime Data Analysis, Visualization and Prediction using Data MiningAnavadya Shibu
This paper presents a general idea about the
model of Data Mining techniques and diverse crimes. It also
provides an inclusive survey of competent and valuable
techniques on data mining for crime data analysis. The
objective of the data mining is to recognize patterns in
criminal manners in order to predict crime anticipate
criminal activity and prevent it. This project implements a
novel data mining techniques like KNN, Text Clustering, IR
tree for investigating the crime data sets and sorts out the
accessible problems. The collective knowledge of various
data mining algorithms tend certainly to afford an
enhanced, incorporated, and precise result over the crime
prediction in the banking sectors Our law enforcement
organizations require to be adequately outfitted to defeat
and prevent the crime. This project is developed using Java
as front-end and MySQL as back-end. Supporting
applications like Sunset, NetBeans are used to make the
portal more interactive.
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.
Identifying Cross Site Scripting Vulnerabilities in Web ApplicationsPorfirio Tramontana
Cross Site Scripting (XSS) is a vulnerability of a Web Application that is essentially caused by the failure of the application to check up on user input before returning it to the client’s web browser. Without an adequate validation, user input may include malicious code that may be sent to other clients and unexpectedly executed by their browsers, thus causing a security attack.
Techniques to prevent this type of attacks require that all application input must be checked up and filtered, encoded, or validated before sending them to any user. In order to discover the XSS vulnerabilities in a Web application, traditional source code analysis techniques can be exploited. In this paper, in order to assess the XSS vulnerability of a Web application, an approach that combines static and dynamic analysis of the Web application is presented. Static analysis based criteria have been defined to detect potential vulnerabilities in the server pages of a Web application, while a process of dynamic analysis has been proposed in order to detect actual vulnerabilities. Some case studies have been carried out, giving encouraging results.
Base Paper Abstract:
Most web applications have critical bugs (faults) affecting their security, which makes them vulnerable to attacks by hackers and organized crime. To prevent these security problems from occurring it is of utmost importance to understand the typical software faults. This paper contributes to this body of knowledge by presenting a field study on two of the most widely spread and critical web application vulnerabilities: SQL Injection and XSS. It analyzes the source code of security patches of widely used web applications written in weak and strong typed languages. Results show that only a small subset of software fault types, affecting a restricted collection of statements, is related to security. To understand how these vulnerabilities are really exploited by hackers, this paper also presents an analysis of the source code of the scripts used to attack them. The outcomes of this study can be used to train software developers and code inspectors in the detection of such faults and are also the foundation for the research of realistic
vulnerability and attack injectors that can be used to assess security mechanisms, such as intrusion detection systems, vulnerability scanners, and static code analyzers.
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C Overflows Vulnerabilities Exploit Taxonomy And Evaluation on Static Analysi...Nurul Haszeli Ahmad
This is a presentation slide presented during mock viva as a requirement from IPSIS, UiTM for Post-graduate student before submitting complete thesis for viva
Detecting Security Vulnerabilities in Web Applications Using Dynamic Analysis...Andrew Petukhov
This talk was given at OWASP AppSec Europe 2008.
Full paper can be downloaded from here:
http://www.owasp.org/images/3/3e/OWASP-AppSecEU08-Petukhov.pdf
WEB APPLICATION VULNERABILITIES: DAWN, DETECTION, EXPLOITATION AND DEFENSEAjith Kp
A slide show on the subject web application vulnerabilities. It contains how the vulnerabilities evolves, how to detect, how to exploit and how to defense against the vulnerabilities with example.
No locked doors, no windows barred: hacking OpenAM infrastructureAndrew Petukhov
One of the main functional components of enterprise applications and Internet portals is an authentication and access control system (AuthC/Z). In this presentation, we describe a popular access control system called ForgeRock OpenAM from the external security point of view. We show the scenarios of full enterprise application compromise through complex attacks which employ both LFI and SSRF.
Attribute-based encryption (ABE) is a public-key based one-to-many encryption that allows users to encrypt and decrypt data based on user attributes.
A promising application of ABE is flexible access control of encrypted data stored in the cloud, using access polices and ascribed attributes associated with private keys and ciphertexts.One of the main efficiency drawbacks of the existing ABE schemes is that decryption involves expensive pairing operations and the number of such operations grows with the complexity of the access policy. Recently, Green et al. proposed an ABE system with outsourced decryption that largely eliminates the decryption overhead for users.
In such a system, a user provides an untrusted server, say a cloud service provider, with a transformation key that allows the cloud to translate any ABE cipher text satisfied by that user’s attributes or access policy into a simple cipher text, and it only incurs a small computational overhead for the user to recover the plaintext from the transformed cipher text.
Security of an ABE system with outsourced decryption ensures that an adversary (including a malicious cloud) will not be able to learn anything about the encrypted message; however, it does not guarantee the correctness of the transformation done by the cloud.
In this paper, we consider a new requirement of ABE with outsourced decryption: verifiability. Informally, verifiability guarantees that a user can efficiently check if the transformation is done correctly.
We give the formal model of ABE with verifiable outsourced decryption and propose a concrete scheme. We prove that our new scheme is both secure and verifiable, without relying on random oracles.
Finally, we show an implementation of our scheme and result of performance measurements, which indicates a significant reduction on computing resources imposed on users.
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In computing, a firewall is a software or hardware-based network security system that controls the incoming and outgoing network traffic based on applied rule set. A firewall establishes a barrier between a trusted, secure internal network and another network (e.g., the Internet) that is not assumed to be secure and trusted.
Many personal computer operating systems include software-based firewalls to protect against threats from the public Internet. Many routers that pass data between networks contain firewall components and, conversely, many firewalls can perform basic routing functions.
This presentation covers data mining within artificial intelligence. Topics covered are as follows: motivation, synonym, process of data mining, operation of data mining, data mining techniques, business application, application selection, and current issues.
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.
Supervised Machine Learning Algorithms for Intrusion Detection.pptxssuserf3a100
Intrusion detection systems using supervised machine learning algorithms are considered one of the most important tools used in the field of information security. These systems analyze data and detect illegal activities and intrusions into networks and systems. These systems rely on machine learning techniques to classify data as either normal activity or a hack. These systems include training and testing phases, where the algorithms are trained on a set of pre-labeled data to learn the natural pattern of the data and distinguish between normal activities and intrusions. Many supervisory machine learning algorithms are available for intrusion detection systems, such as Gaussian Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression.
The problem of security and electronic breaches targeting networks is one of the biggest problems facing organizations today. To solve this problem, intrusion detection systems (IDS) and their tools can be used to detect and prevent these threats. This file provides an introductory overview of this problem
Security breaches
networks
Infiltration (IDS)
Algorithms
Machine learning
Detecting Unknown Attacks Using Big Data AnalysisEditor IJMTER
Nowadays threat of previously unknown cyber-attacks are increasing because existing security
systems are not able to detect them. Previously, leaking personal information by attacking the PC or
destroying the system was very common cyber attacks . But the goal of recent hacking attacks has changed
from leaking information and destruction of services to attacking large-scale systems such as critical
infrastructures and state agencies. In the other words, existing defence technologies to counter these attacks
are based on pattern matching methods which are very limited. Because of this fact, in the event of new and
previously unknown attacks, detection rate becomes very low and false negative increases. To defend
against these unknown attacks, which cannot be detected with existing technology, a new model based on
big data analysis techniques that can extract information from a variety of sources to detect future attacks is
proposed. The expectation with this model is future Advanced Persistent Threat (APT) detection and
prevention.
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.
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.
Deep Learning based Threat / Intrusion detection systemAffine Analytics
The article is about a Threat/Intrusion Detection System, which could be used to detect such data leaks/breaches & take a preventive action to contain, if not stop the damage due to breach.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
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.
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.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
Similar to data mining for security application (20)
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
4. Natural Disasters Human Errors Non - Information related threats Information Related threats Biological, Chemical, Nuclear Threats Critical Infrastructure Threats Threat Types
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18. Integrate data sources Clean/ modify data sources Build Profiles of Terrorists and Activities Examine results/ Prune results Report final results Data sources with information about terrorists and terrorist activities Mine the data
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20. Integrate data sources in real - time Build real - time models Examine Results in Real - time Report final results Data sources with information about terrorists and terrorist activities Mine the data Rapidly sift through data and discard irrelevant data
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