Computational intelligence in intrusion detection systems

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1 Computational intelligence in intrusion detection systems --- An introduction to an introduction Rick TEIL

2 Reference The use of computational intelligence in intrusion detection systems : A review Shelly Xiaonan Wu, Wolfgang Banzhaf Applied Soft Computing 2009

3 Intrusion prevention techniques Intrusion detection systems (IDS) Fire walls Access control Encryption Data collection Data preprocessing Intrusion recognition Reporting Response

4 History of IDS 1987 early 1990s late 1990s D.E. Denning proposed an intrusion detection model Combinations of expert systems and statistical approaches Automated knowledge acquisition Combine with computational intelligence

5 Computational intelligence J.C. Bezdek (1994) : A system is computational intelligent when it: deals with only numerical (low-level) data, has pattern recognition components does not use knowledge in the artificial intelligence sense; and additionally when it (begins to) exhibit (i) computational adaptivity, (ii) computational fault tolerance, (iii) speed approaching human-like turnaround, and (iv) error rates that approximate human performance.

6 Computational intelligence Artificial neural networks Fuzzy sets Evolutionary computation methods Artificial immune systems Swarm intelligence Soft computing.

7 Roadmap Introduction to intrusion detection systems (IDS) Evolutionary computation methods Artificial immune systems Swarm intelligence Discussion

8 Intrusion detection system Solid lines : data/control flow Dashed lines : responses to intrusive activities.

9 Intrusion detection system IDS Misuse detection Anomaly detection Predefined descriptions of intrusive behaviors Supervised learning Fail easily when facing unknown intrusions Hypothesize that abnormal behavior is rare and different from normal behavior Unsupervised learning Difficulties: deficiency of abnormal samples, adaption to constantly changing normal behavior

10 Evolutionary computation

11 Evolutionary computation Genetic algorithms Automatic model structure design Classifiers Genetic programming Classifiers

12 Automatic model structure design Artificial neural networks need optimal structures. Clustering algorithms need the number of clusters. Use GA to search the right structure or parameters

13 Classifiers Classification rules Transformation functions GA: search the parameters GP: search the functions

14 Niching and fitness function Niching techniques are adopted. Fitness sharing, crowding, voting, token competition Fitness function Detection rate False positive rate Conciseness

15 Challenges No reasonable termination criterion Niching Distributed EC models Unbalanced data distribution

16 Artificial immune system

17 Human immune system Innate immune system Adaptive immune system

18 Innate immune system 1. Skin 2. Respiratory tract 3. Gastrointestinal tract 4. Urogenital tract ***CORPORATION 1. Phagocytosis 2. Inflammation 3. Complement 4. Interferon

19 Adaptive immune system 1. Skin 2. Respiratory tract 3. Gastrointestinal tract 4. Urogenital tract ***CORPORATION 1. Phagocytosis 2. Inflammation 3. Complement 4. Interferon

20 ***CORPORATION Adaptive immune system

21 T-cell helper IL-1 IL-2 killer supressor memory M T4 IL-6 Plasm cell B-cell Ig ***CORPORATION memory

22 Normally, lymphocytes do not attack normal cells, why? Lymphocytes must be mature before leaving red bone marrow.

23 Maturation To avoid autoimmunity, T cells and B cells must pass a negative selection stage, where lymphocytes which match self cells are killed. (These mature lymphocytes have never encountered antigens.)

24 Artificial immune system (AIS) Anomaly detection Instead of building models for the normal, they generate non-self (anomalous) patterns by giving normal data.

25 Negative selection

26 Self non-self discrimination model

27 Lifespan model

28 An evolutionary AIS model Three stages : gene library evolution negative selection clonal selection Immature detectors, rather than generated randomly, are created by selecting and rearranging useful genes. The library evolves. The clonal selection detects various intrusions with a limited number of detectors, generates memory detectors, and drives the gene library evolution.

29

30 Challenges Fitting to real-world environments Avoid the scaling problem Detect and fill holes Estimate the coverage of rule sets Deal with a high volume and dimensional data Adapting to changes in self data Integrating immune responses

31 Swarm intelligence

32 Ant colony optimization Use ACO to keep track of intruder trails Identify affected paths of intrusion in a sensor network by investigating the pheromone concentration Clustering local strategy rules

33 Particle swarm optimization Learn classification rules divide-and-conquer : Use PSO to find the best rule covering current training set Remove those covered points

34 Discussion

35 Performance

36 Research

37 Challenges Good benchmark datasets Old and unrealistic Ability of adaptation to constantly changing environments intrusive behavior legitimate behavior systems networks

38 Thanks

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