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Intrusion detection via fuzzy-genetic algorithm combination with evolutionary algorithms

Toroghi Haghighat, T ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ICIS.2007.124
  3. Publisher: 2007
  4. Abstract:
  5. In this paper with the use of fuzzy genetic algorithm combination with evolutionary algorithms, as a method for local searching, it has been tried to exploit high capabilities of genetic algorithm, as a search algorithm, beside to other evolutionary algorithms, as local search algorithms, in order to increase efficiency of a rule learning system. For this purpose three hybrid algorithms have been used for solving the intrusion detection problem. These three algorithms are combination of genetic algorithm and SFL and PSO as three evolutionary algorithms which try to introduce efficient solutions for complex optimization problems by patterning from natural treatments. © 2007 IEEE
  6. Keywords:
  7. Algorithms ; Boolean functions ; Communication ; Cybernetics ; Diesel engines ; Fuzzy logic ; Genetic algorithms ; Information management ; Information science ; Intrusion detection ; Knowledge management ; Learning algorithms ; Learning systems ; Particle swarm optimization (PSO) ; (1+1) evolutionary algorithms ; Complex optimization ; Efficient solutions ; Fuzzy genetic algorithm (FGA) ; Hybrid algorithm (HA) ; In order ; International (CO) ; International conferences ; Local search (LS) algorithm ; Local searching ; Rule learning ; Search algorithms ; Evolutionary algorithms
  8. Source: 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007, Melbourne, VIC, 11 July 2007 through 13 July 2007 ; July , 2007 , Pages 587-591 ; 0769528414 (ISBN); 9780769528410 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4276445