Engineering Applications of Artificial Intelligence,

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A parallel genetic local search algorithm for intrusion detection in computer networks Engineering Applications of Artificial Intelligence, Vol. 20, Page 1058-1069, Dec. 2007 Authors：Mohammad Saniee Abadeh, Jafar Habibi, Zeynab Barzegar and Muna Sergi Present：Jheng-Hen Jiang 2010/7/22

Outline Introduction Related Work Proposed Scheme Experimental Result Conclusions 2010/7/22

Introduction Finding high-quality fuzzy if-then rules to predict the class of input patterns correctly. Generating low false alarms. 2010/7/22

Related Work Genetic algorithm. Pittsburgh approach. Michigan approach. 2010/7/22

Proposed Scheme(1/7) 2010/7/22

Proposed Scheme(2/7) Initialization Selection Local Search Crossover Fuzzy Rule Set Pool Replacement Mutation Reinitialization Internal Termination Test External Termination Test 2010/7/22 6

Proposed Scheme(3/7) Initialization 2010/7/22

Proposed Scheme(4/7) Selection 2010/7/22

Crossover and Mutation Proposed Scheme(5/7) Crossover and Mutation Crossover One-point crossover. Mutation Mrepeat = 50 2010/7/22

Proposed Scheme(6/7) Local search Fitness(Rj) > Threshold It’s a fuzzy rule Change attribute’s value Reject 2010/7/22

Proposed Scheme(7/7) Replace and Reinitialization Prep = Replacement percentage of the classifier system. Fitness(Rj) > Threshold Change attribute’s value 2010/7/22

Experimental Result(1/4) 2010/7/22

Experimental Result(2/4) 2010/7/22

Experimental Result(3/4) 2010/7/22

Experimental Result(4/4) 2010/7/22

Conclusions It can increasing the detection rate and decreasing the false alarm rate. Training time is decreased by using the suggested parallel learning framework. Every sub dataset’s class are all the same. 2010/7/22