چكيده به لاتين
Nowadays, due to the increasing use of various types of networks by human, the need for security in these networks is increasing. So that it is tried to establish three components of confidentiality, integrity and accessibility in the network by different methods. As an essential defense technique, intrusion detection systems become more and more popular. Several machine learning algorithms exist that can use to model an intrusion detection system. Increasing the complexity and volume of data, makes it more difficult for intrusion detection systems to detect intrusions. Therefore, the performance of intrusion detection systems has always been one of the challenges in the field of network security. One of the problems that intrusion detection systems face is reducing the detection rate in the face of new attacks and increasing the error when working with big data. To solve these problems, different machine learning methods can be useful. Intrusion detection systems need to analyze large volumes of information at high speed, this amount of information may reduce the detection rate in these systems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. Many support vector machine (SVM)-based intrusion detection algorithms have been widely used to identify an intrusion quickly and accurately. In this thesis, we propose an algorithm based on genetic algorithm and support vector machine. The algorithm first uses feature selection method based on genetic algorithm with an innovative fitness function that decreases the error rate and increases the true positive rate and accuracy. Then, according to the optimal feature subset, that the genetic algorithm has selected, we run the intrusion detection algorithm. This algorithm contains two levels of support vector machine. One of them is two-class SVM and the other one is multi-class SVM. The performance of our proposed algorithm is evaluated using the KDD-Cup99 dataset. The simulation results show that, the algorithm in comparison with the intrusion detection system including a support vector machine can reduce the error by 32%. In reference [1] support vector machine and genetic algorithm have been used to design the intrusion detection system, the proposed algorithm has been able to increase the accuracy of the system by 2.4% compared to this reference and has a positive effect on detection rate. At the end, we investigate the performance of algorithm in semi-supervised mode and compare the results with supervised mode.
