A novel hybrid GA and SVM with PSO feature selection for intrusion detection system

The computer network technologies are evolving fast, and the development of internet technology is more quickly, people more aware of the importance of the network security. Network security is the main issue of computing because the numbers of attacks are continuously increasing. For these reasons, intrusion detection systems (IDSs) have emerged as a group of methods that combats the unauthorized use of a network’s resources. Recent advances in information technology, especially in data mining, have produced a wide variety of machine learning methods, which can be integrated into an IDS. This study proposes a new method of intrusion detection that uses support vector machine optimizing by a genetic algorithm. to improve the efficiency of detecting known and unknown attacks, we used a Particle Swarm Optimization algorithm to select the most influential features for learning the classification model.