Title :
Improved Genetic Algorithm in Intrusion Detection Model Based on Artificial Immune Theory
Author :
Jing Xiaopei ; Wang Houxiang ; Han Ruofei ; Li Juan
Author_Institution :
Inf. & Electr. Coll., Naval Univ. of Eng., Wuhan, China
Abstract :
After analysis the characteristics of AlS-based intrusion detection system, a new AlS-based intrusion detection model based improved genetic algorithm is established. By utilizing prominent characteristics of genetic algorithm, such as automatic optimizing, global researching, and adaptability, the new model uses genetic operator to improve the candidate detectors generating algorithm and reduce detectors redundancy. The detectors generated by new model have good fitness and better detection ability. Experiments show that this model can effectively increase the true positive rate of the IDS.
Keywords :
artificial immune systems; genetic algorithms; security of data; AlS-based intrusion detection system; artificial immune theory; genetic operator; improved genetic algorithm; intrusion detection model; Detectors; Educational institutions; Flowcharts; Genetic algorithms; Genetic engineering; Genetic mutations; Immune system; Information analysis; Intrusion detection; Random number generation;
Conference_Titel :
Computer Network and Multimedia Technology, 2009. CNMT 2009. International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5272-9
DOI :
10.1109/CNMT.2009.5374541