Toward an Appropriate Approach for Intelligent Intrusion and Detection Systems

Abstract

Now a days, the company’s information security become among a main priority. Indeed, the more the attack force on the network develops, the more it is necessary to develop the security and the network surveillance. The data is to be exchanged between the internal company network and the outside one such as Internet. It is therefore necessary to be protected against malicious intrusions into the company's network, but also to monitor the traffic inside the network in order to prevent possible internal attacks. Currently, security and reliability have become the major concerns of an individual or organization. A rule-based intrusion detection system (IDS) called Snort is an open-source software used as a network protection tool that can only detect recognized attacks. In order to detect advanced network attacks and detect fraudulent network traffic, this research paper proposes an advanced and more intelligent approach by applying machine learning. To find the best algorithm to use with Snort to improve its detection, the support vector machine (SVM) was chosen based on its accuracy. The proposed system has produced efficient detection rates versus other proposed approaches in the security intrusions detection field.