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

Subject Areas : Renewable energy

Elaheh Faghihnia ¹ , Seyed Reza Kamel Tabakh Farizni ² , Maryam Kheirabadi ³

1 - Department of Computer- Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
2 - Department of Computer Engineering- Mashhad Branch, Islamic Azad University, Mashhad, Iran
3 - Department of Computer- Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

Received: 2020-10-15 Accepted : 2020-12-16 Published : 2021-05-22

Keywords: Support vector machine, Intrusion Detection Systems, Island Genetic Algorithm, Big data, distributed self-adaptive genetic algorithm, self-adaptive genetic algorithm,

Abstract :

Intrusion into systems through network infrastructure and the Internet is one of the security challenges facing the world of information and communication technology and can lead to the destruction of systems and access to data and information. In this paper, a support vector machine model with weighted and parameters of SVM kernels are presented to detect the intrusion. 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 (DSAGA). On the other hand, due to the high volume of data in such issues, Auto encoder has been used to reduce data. The proposed approach is a hybrid method based on Auto encoder, improved Support Vector Machine and Distributed Self Adaptive Genetic Algorithm (DSAGA) that it is evaluated by its execution on DARPA data set.

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