Presentation on theme: "Fuzzy Learning Classifier System for Intrusion Detection Monu Bambroo."— Presentation transcript:
Fuzzy Learning Classifier System for Intrusion Detection Monu Bambroo
Motivation Total revenue losses in 2002 due to network breaches were about $10 billion. Computer security problem is inherently modeling in nature. Fuzzy logic is robust with respect to modeling imprecision and vagueness
Inductive Learning Inductive learning is learning by example. C4.5 program constructs classifiers in the form of a decision tree. Decision trees are sometimes too complex to understand. C4.5 re-expresses the classification model as production-rules.
Experimental Data Set KDD’99 dataset was used for the experiments. Each connection in the dataset is labeled as either normal or an attack type with exactly one specific attack type. Attacks fall into 4 main categories. – DOS – R2L – U2R – Probing R2L attack warez-master is our experimental attack- type.
Crisp Versus Fuzzy Sets Close Distance[mm] MediumFarμ Crisp Set Fuzzy Set Distance[mm] μ CloseMediumFar
What is a ‘Learning Fuzzy Classifier System’ (LFCS) Learn rules where clauses are labels associated with fuzzy sets Each fuzzy set represents a membership function for a variable A Genetic algorithm operates on fuzzy sets evolving best solution
Comparing ‘LCS’ and ‘LFCS’ Matching Rule Activation Reinforcement Distribution Genetic Algorithm
Rule Base Representation Type : 1 If (duration is 7) and (srcbytes is 6) and (hot is 3) then (attack is ware-master) (1)
Contd. Rules are represented using the ‘Michigan Approach’ Pittsburgh requires large amount of computational effort Genetic activity destroys local optimum In Michigan approach, genetic operator operate on single rules
Reinforcement Distribution Fuzzy Bucket Brigade Algorithm I.Compute the bid basing on action sets of active classifier II.Reduce strength of active classifiers by a quantity equal to its contribution to the bid III.Distribute the bid to classifier belonging to action set which led to reward.