A Clustering Approach for Intrusion Detection with Big Data Processing on Parallel Computing Platform

Abstract

In recent years there is a growing number of attacks in the computer networks. Therefore, the use of a prevention mechanism is an inevitable need for security admins. Although firewalls are preferred as the first layer of protection, it is not sufficient for preventing lots of the attacks, especially from the insider attacks. Intrusion Detection Systems (IDSs) have emerged as an effective solution to these types of attacks. For increasing the efficiency of the IDS system, a dynamic solution, which can adapt itself and can detect new types of intrusions with a dynamic structure by the use of learning algorithms is mostly preferred. In previous years, some machine learning approaches are implemented in lots of IDSs. In the current position of artificial intelligence, most of the learning systems are transferred with the use of Deep Learning approaches due to its flexibility and the use of Big Data with high accuracy. In this paper, we propose a clustered approach to detect the intrusions in a network. Firstly, the system is trained with Deep Neural Network on a Big Data set by accelerating its performance with the use of CUDA architecture. Experimental results show that the proposed system has a very good accuracy rate and low runtime duration with the use of this parallel computation architecture. Additionally, the proposed system needs a relatively small duration for training the system.