TY - EJOUR
AU - Shirazi, Hossein
PY - 2024
DA - February
TI - An Intelligent Intrusion Detection System Using Genetic Algorithms and Features Selection
T2 - Majlesi Journal of Electrical Engineering
VL - 4
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/an-intelligent-intrusion-detection-system-using-genetic-algorithms-and-features-selection/
DO - 10.1234/mjee.v4i1.154
N2 - There has been a rapid growth in the numbers of attacks to the information and communication systems. Also, we witness smarter behaviors from the attackers. Thus, to prevent our systems from these attackers, we need to create smarter intrusion detection systems. In this paper, a new intelligent intrusion detection system has been proposed using genetic algorithms. In this system, at first, the network connection features were ranked according to their importance in detecting attack using information theory measures. Then, the network traffic linear classifiers based on genetic algorithms have been designed. These classifiers were trained and tested using KDD99 data sets. A detection engine based on these classifiers was build and experimented. The experimental results showed a detection rate up till to 92.94%. This engine can be used in real-time mode.
IS - 1
PB - OICC Press
KW - Intrusion Detection Systems, Anomaly detection. genetic algorithms, Features selection, Security, Computer science 
EN -