1. A parallel genetic local search algorithm for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence,
Vol. 20, Page , Dec. 2007
Authors：Mohammad Saniee Abadeh, Jafar Habibi,
Zeynab Barzegar and Muna Sergi
Present：Jheng-Hen Jiang
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2. Outline Introduction Related Work Proposed Scheme Experimental Result
Conclusions
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3. Introduction
Finding high-quality fuzzy if-then rules to predict the class of input patterns correctly.
Generating low false alarms.
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4. Related Work Genetic algorithm. Pittsburgh approach.
Michigan approach.
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5. Proposed Scheme(1/7)
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6. Proposed Scheme(2/7) Initialization Selection Local Search Crossover
Fuzzy Rule Set Pool
Replacement
Mutation
Reinitialization
Internal Termination Test
External Termination Test
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7. Proposed Scheme(3/7)
Initialization
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8. Proposed Scheme(4/7)
Selection
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9. Crossover and Mutation
Proposed Scheme(5/7)
Crossover and Mutation
Crossover
One-point crossover.
Mutation
Mrepeat = 50
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10. Proposed Scheme(6/7) Local search Fitness(Rj) > Threshold
It’s a fuzzy rule
Change attribute’s value
Reject
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11. Proposed Scheme(7/7) Replace and Reinitialization
Prep = Replacement percentage of the classifier system.
Fitness(Rj) > Threshold
Change attribute’s value
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12. Experimental Result(1/4)
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13. Experimental Result(2/4)
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14. Experimental Result(3/4)
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15. Experimental Result(4/4)
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16. 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.
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