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Intrusion Detection

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  • 1. Intrusion Detection

2. Outline Introduction A Frame for Intrusion Detection System Intrusion Detection Techniques Ideas for Improving Intrusion Detection 3. What is the Intrusion Detection Intrusionsare the activities that violate the security policy of system. Intrusion Detection is the process used to identify intrusions. 4. Types of Intrusion Detection System(1) Based on the sources of the audit information used by each IDS, the IDSs may be classified into Host-base IDSs Distributed IDSs Network-based IDSs 5. Types of Intrusion Detection System(2) Host-basedIDSs Get audit data from host audit trails. Detect attacks against a single host DistributedIDSs Gather audit data from multiple host and possibly thenetwork that connects the hosts Detect attacks involving multiple hosts Network-BasedIDSs Use network traffic as the audit data source, relievingthe burden on the hosts that usually provide normal computing services Detect attacks from network. 6. Intrusion Detection Techniques Misusedetection Catch the intrusions in terms of thecharacteristics of known attacks or system vulnerabilities. Anomalydetection Detect any action that significantly deviatesfrom the normal behavior. 7. Misuse Detection Basedon known attack actions. Feature extract from known intrusions Integrate the Human knowledge. The rules are pre-defined Disadvantage: Cannot detect novel or unknown attacks 8. Misuse Detection Methods & System Method System Rule-based Languages RUSSEL,P-BEST State Transition Analysis STAT family(STAT,USTAT,NS TAT,NetSTAT) Colored Petri Automata Expert SystemIDIOT IDES,NIDX,PBEST,ISOACase Based reasoningAutiGUARD 9. Anomaly Detection Basedon the normal behavior of a subject. Sometime assume the training audit data does not include intrusion data. Any action that significantly deviates from the normal behavior is considered intrusion. 10. Anomaly Detection Methods & System Method Statistical methodSystem IDES, NIDES, EMERALDMachine Learning techniques Time-Based inductive Machine Instance Based Learning Neural Network Data mining approachesJAM, MADAM ID 11. Anomaly Detection Disadvantages Basedon audit data collected over a period of normal operation. When a noise(intrusion) data in the trainingdata, it will make a mis-classification. Howto decide the features to be used. The features are usually decided by domain experts. It may be not completely. 12. Misuse Detection vs. Anomaly Detection AdvantageDisadvantageMisuse DetectionAccurately and generate much fewer false alarmCannot detect novel or unknown attacksAnomaly DetectionIs able to detect unknown attacks based on auditHigh false-alarm and limited by training data. 13. The Frame for Intrusion Detection 14. Intrusion Detection Approaches Define and extract the features of behavior in system 2. Define and extract the Rules of Intrusion 3. Apply the rules to detect the intrusion 1.Audit Data 3 Training Audit Data1Features2Rules3Pattern matching or Classification 15. Thinking about The Intrusion Detection System Intrusion Detection system is a pattern discover and pattern recognition system. The Pattern (Rule) is the most important part in the Intrusion Detection System Pattern(Rule) Expression Pattern(Rule) Discover Pattern Matching & Pattern Recognition. 16. Machine Learning & Data mining & Statistics methods Traning Audit DataFeature ExtractionTraining Data & Knowled gePattern Extraction Expert Knowledge & Rule collection & Rule abstractionPattern & Decision Rule Pattern MatchingAlarmsIntrusion Detection SystemDiscriminate functionPassPattern RecognitionReal-Time Aduit data 17. Rule Discover Method ExpertSystem Measure Based method Statistical method Information-Theoretic Measures Outlier analysis DiscoveryAssociation Rules Classification Cluster 18. Pattern Matching & Pattern Recognition Methods PatternMatching State Transition & Automata Analysis Case Based reasoning Expert System Measure Based method Statistical method Information-Theoretic Measures Outlier analysis AssociationPattern Machine Learning method 19. Intrusion Detection Techniques 20. Intrusion Detection Techniques PatternMatching Measure Based method Data Mining method Machine Learning Method 21. Pattern Matching KMP-Multiplepatterns matching Algorithm Using keyword tree to search Building failure link to guarantee linear time searching Shift-And(Or)pattern matching Algorithm A classical approximate pattern matching algorithm Karp-Rabinfingerprint method Using the Modular arithmetic and Remainder theoremto match pattern(Such as regular expression pattern matching) 22. Measure Based Method Statistical Methods & Information-Theoretic Measures Definea set of measures to measure different aspects of a subject of behavior. (Define Pattern) Generate an overall measure to reflect the abnormality of the behavior. For example: statistic T2= M12+M22 ++Mn2 weighted intrusion score = Mi*Wi Entropy: H(X|Y)= P(X|Y) (-log(P(X|Y))) Definethe threshold for the overall measure 23. Association Pattern Discover Goalis to derive multi-feature (attribute) correlations from a set of records. An expression of an association pattern:The Pattern Discover Algorithm: 1. 2.Apriori Algorithm FP(frequent pattern)-Tree 24. Association Pattern Example 25. Association Pattern Detecting Statistics Approaches Constructing temporal statistical features fromdiscovered pattern. Using measure-based method to detect intrusion PatternMatching Nobody discuss this idea. 26. Machine Learning Method Time-BasedInductive Machine Like Bayes Network, use the probability and adirect graph to predict the next event InstanceBased Learning Define a distance to measure the similaritybetween feature vectors Neural Network 27. Classification Thisis supervised learning. The class will be predetermined in training phase. Define the character of classes in training phase. A common approach in pattern recognition system 28. Clustering Thisis unsupervised learning. There are not predetermined classes in data. Given a set of measurement, the aim is that establishes the class or group in the data. It will output the character of each class or group. In the detection phase, this method will get more time cost (O(n2)). I suggest this method only use in pattern discover phase 29. Ideas for improving Intrusion Detection 30. Idea 1: Association Pattern Detecting Usingthe pattern matching algorithm to match the pattern in sequent data for detecting intrusion. No necessary to construct the measure. But its time cost is depend on the number of association patterns. It possible constructs a pattern tree to improve the pattern matching time cost to linear time 31. Idea 2: Discover Pattern from Rules Theexist rules are the knowledge from experts knowledge or other system. The different methods will measure different aspects of intrusions. Combine these rules may find other new patterns of unknown attack. For example: Snort has a set of rule which come from different people.The rules may have different aspects of intrusions. We can use the data mining or machine learning method to discover the pattern from these rule. 32. Machine Learning & Data mining & Statistics methods Traning Audit DataFeature ExtractionTraining Data & Knowled gePattern Extraction Expert Knowledge & Rule collection & Rule abstractionPattern & Decision Rule Pattern MatchingAlarmsIntrusion Detection SystemDiscriminate functionPassPattern RecognitionReal-Time Aduit data 33. Reference Lee, W., & Stolfo, S.J. (2000). A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security, 3 (4) (pp. 227-261). Jian Pei,Data Mining for Intrusion Detection:Techniques,Applications and Systems, Proceedings of the 20th International Conference on Data Engineering (ICDE 04) Peng Ning and Sushil Jajodia,Intrusion Detection Techniques. From Snort---The open source intrusion detection system. (2002). Retrieved February 13, 2003, from 34. Thank you!