Improved Intrusion Detection System Based On Distributed Self-Adaptive Genetic Algorithm to Solve Support Vector Machine in Form of Multi Kernel Learning with Auto Encoder

Today easy data access through the network has made it possible to steal them. Therefore, the security of computer systems has become increasingly important. Intrusion Detection Systems . as the last line of computer defense, can play an important role in attack resistance and their efficiencies has direct impact on network security. The Intrusion Detection Systems must extract the necessary strategies based on the connections and use them to detect new connections. Support Vector Machine is a Machine Learning method that it is popular to extract intrusion strategies in past decade. Although simplification of SVM returned it to popular method but it has constraints such as senility to kernel selection and it has not any optimization mechanism to determine the best of them. We model it as using of several kernels simultaneously and different weighting to them and dynamic SVM parameters. Due to the high complexity of this problem, conventional optimization methods are not able to solve it. Therefore, we propose a Distributed Self Adaptive Genetic Algorithm with Migration. On the other hand, due to the high volume of data in such issues, Autoencoder has been used to reduce data. The proposed approach is a hybrid method based on Autoencoder and improved Support Vector Machine with Distributed Self Adaptive Genetic Algorithm with Migration that it is evaluated by its execution on data set. The experimental results have demonstrated that the proposed system exhibits a high performance for attack detection based on precision and recall and it low time for intrusion.