Data mining in Cyber security
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This document discusses using data mining techniques for intrusion detection in cyber security. It defines cyber security and cyber crimes, and explains how data mining can help with intrusion detection. Specifically, it describes how classification methods like neural networks and clustering can be used to detect anomalies and build models of normal network activity that may help identify intrusions. The goal of these data mining approaches for intrusion detection is to analyze network traffic data to learn patterns and identify both known and unknown attacks.
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Role of data mining in cyber security
This document discusses the role of data mining in cyber security and intrusion detection. It begins with defining cyber security and cyber crimes. It then discusses how data mining can help with intrusion detection by applying algorithms to network traffic data to identify abnormal activities and security threats. Specifically, it outlines how classification methods like neural networks and clustering can be used to detect malware, build models of normal network behavior, and identify deviations that may indicate security issues. The goal is to use data mining to help detect a wide range of intrusions in a timely manner.
Role of data mining in cyber security
Seminar:
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https://www.shamra.sy/academia/show/5b0bff1c5de6d
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Mohammad Fadi Taqi Al-Din
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Ahmad Al-Yassin
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Intrusion detection
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Role of data mining in cyber security
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This document provides an overview of cybersecurity, including its history from the 1990s to present. It discusses cybersecurity landscapes, concepts, education, international and US perspectives, legal issues, and threat actors and trends. The key topics covered include the emergence of cybersecurity as a field, different frameworks for cybersecurity including those from NIST and CIAC, the importance of cybersecurity education and standardization, cyber attacks posing risks to critical infrastructure, and the challenges of applying law to this evolving domain across international boundaries.
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This slide is a small introduction for cyber security.
What is cyber security?
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What are the benefits of cybersecurity?
Types of cyber security threats
How to prevent the breaches?
Some real attacks
IDS and IPS
An intrusion detection system (IDS) monitors network traffic and system activities for suspicious activity that could indicate a security threat or attack. An IDS analyzes patterns in traffic to identify potential threats. There are network IDS that monitor entire network traffic and host IDS that monitor individual systems. An IDS detects threats but does not prevent them. An intrusion prevention system (IPS) can detect and prevent threats by blocking malicious traffic in real-time. An IPS combines IDS detection capabilities with preventative blocking functions. Common types of IPS include inline network IPS, layer 7 switches, application firewalls, and hybrid switches.
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An Intrusion Detection based on Data mining technique and its intended import...
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.
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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
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Intrusion Detection & Prevention Systems (IDPS) are crucial for protecting computers and detecting threats in real time. As threats have grown in the 21st century, IDPS have also evolved, with different types providing various protection functions. Effective IDPS not only detect and prevent attacks, but also log events, create reports on recent attacks, and provide detailed information. Detection methods include signature-based detection by comparing traffic to known attacks, anomaly-based detection by identifying deviations from normal behavior, and policy-based detection by enforcing allowed functions.
The Practical Data Mining Model for Efficient IDS through Relational Databases
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.
Intrusion detection system: classification, techniques and datasets to implement
This document discusses intrusion detection systems, including their classification techniques and datasets used to implement them. It first defines intrusion detection and intrusion detection systems, explaining that they monitor networks and systems for malicious activity and policy violations. It then categorizes intrusion detection systems as either network-based, host-based, or physical-based. The document also classifies intrusion detection approaches as either signature-based (misuse detection) or anomaly-based detection. It reviews several data mining techniques used for intrusion detection, including classification methods like decision trees, k-nearest neighbors, naive Bayes, and support vector machines. It also discusses clustering techniques. Finally, it mentions some commonly used intrusion detection datasets.
An Extensive Survey of Intrusion Detection Systems
This document summarizes an extensive survey of intrusion detection systems. It discusses the general architecture of IDS, including host-based and network-based systems. It describes different types of attacks (e.g. DoS, probing, user-to-root) and defenses. It analyzes previous work applying data mining techniques like machine learning to improve detection rates and reduce false alarms. A key problem is the massive number of false alarms that overburden security managers; the document aims to investigate solutions to lower the false alarm rate so that real threats are not missed.
Self Monitoring System to Catch Unauthorized Activity
The document describes a proposed self-monitoring system called SMS that detects unauthorized insider activity on a system. SMS monitors user activity at the system call level and creates user profiles to track normal usage patterns. It compares current activity to these profiles to identify anomalous behavior that may indicate a malicious intrusion. When SMS detects potential unauthorized activity, it takes a snapshot of the event and reports it to administrators. The system aims to improve on other intrusion detection systems by identifying insider threats in real-time at the system call level using data mining and forensic techniques.
A Hybrid Intrusion Detection System for Network Security: A New Proposed Min ...
A Hybrid Intrusion Detection System for Network Security: A New Proposed Min ...IJCSIS Research Publications
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paperAN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
D0261019025
This document summarizes and characterizes trends in data mining techniques for intrusion detection systems. It discusses how data mining can help address limitations in traditional intrusion detection by identifying anomalies, false alarms, and patterns across data. The document then characterizes several data mining techniques that have been applied for intrusion detection, including feature selection, statistical techniques, machine learning approaches like classification and clustering, and specific algorithms like decision trees, genetic algorithms, fuzzy logic, neural networks, and support vector machines.
Bt33430435
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.
Bt33430435
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
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Enhanced method for intrusion detection over kdd cup 99 dataset
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Data mining in Cyber security
- 1. Data Mining in Cyber Security Intrusion Detection Presented by : Sagar Deepak Thapa Guided By : Prof Nagaraju Bogiri KJ College Of Engineering And Management Research Pune 4072
- 2. Outline What is Cyber Security? What is Cyber Crime? Applications of Data Mining in Cyber Security. Intrusion detection. Why Can Data Mining Help? Data Mining approaches for Intrusion Detection. Conclusion.
- 3. Cyber Security Set of technologies and processes designed to protect computers, networks, programs, and data from attack, unauthorized access, change, or destruction. A Majorpart of Cyber Security is to fix broken Software. Cyber Security Computer SecuritySystem Network SecuritySystem
- 4. Cyber Crime Encompasses anycriminal act dealingwith computers and networks. Include: • Malicious programs. • Illegal imports. • Computers Vandalism.
- 5. Cyber Security VS CyberCrime Cyber Security CyberCrime CyberSecurity Cyber Crime One side of the coin Other side of the coin
- 6. Applications of Data Mining in Cyber Security Malwaredetection. Intrusion detection. Fraud detection.
- 7. Intrusion Detection The process of monitoring the events occurring in a computer systemor network and analyzing them for signs of intrusion.
- 8. Intrusion Detection System (IDS) Combination of software and hardware that attempts to perform intrusion detection. Raise the alarm when possible intrusion happens. Steps: Monitoring and analyzing traffic. Identifying abnormal activities. Assessing severity and raisingalarm.
- 9. Detector – ID Engine Response Component Data gathering (sensors) Raw data Information Source - Monitored System Events Knowledge base Configuration Alarms Actions SystemState System State Intrusion Detection System Architecture
- 10. Goals of Intrusion Detection System (IDS) Detect wide variety of intrusions. Detect intrusions in timelyfashion. Present analysis in simple, easy-to-understand format. Be accurate.
- 11. WhyWeNeed Intrusion Detection? Security mechanisms always have inevitable vulnerabilities. Multiple levels of data confidentiality in commercial and government organizations needs multi-layer protection in firewalls.
- 12. Why Can Data MiningHelp? Successful applications in related domains, e.g., fraud detection, fault/alarm management. Learn from traffic data Maintain or update models on dynamic data. Data mining: applying specific algorithms to extract patterns from data. From the data-centric point view , intrusion detection is a data analysisprocess.
- 13. Data Mining approaches for Intrusion Detection
- 14. Classification Methods Neural networks. Bayesian classification. Support vector machines.
- 15. Email Worm Detection Using Data Mining Outgoing Emails TrainingData TestData Classifier Feature Extraction Machine Learning Themodel CleanorInfected
- 16. Clustering Group data into clusters ClusteringApproaches • K-means • Hierarchical Clustering
- 17. Clustering for Intrusion Detection Anomaly detection. Any significant deviations from the expected behavior are reported as possible attacks. Build clusters as models for normal activities.
- 18. Conclusion Data mining has great potential as a malware detection tool. It allows you to analyze huge sets of information and extract new knowledge from it. The main benefit of using data mining techniques for detecting malicious software is the ability to identify both known and zero-day attacks.
- 19. THANK YOU