Lightweight IDS Based on Features Selection and IDS Classification Scheme

The intrusion detection system (IDS) deals with huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. To overcome these limitations, we introduce the concept of lightweight IDS. The lightweight IDSs are small, powerful, and flexible enough to be used as permanent elements of the network security infrastructure. In this paper, we propose a novel concept for building lightweight IDS based on two different approaches. The first approach is using a features selection approach by applying fuzzy enhanced support vector decision function (Fuzzy ESVDF) algorithm. This approach is able to improve system efficiency. The second approach is using IDS classification scheme. The IDS classification scheme divides the detection process into four types according to the TCP/IP network model (application layer IDS, transport layer IDS, network layer IDS, and link layer IDS). This IDS classification scheme enhances an organizationpsilas ability to detect most types of attack (improving system accuracy and generality). Also, it improves IDS scalability and extendibility. To design the proposed system, several experiments have been conducted, and they indicate that the proposed lightweight IDS can deliver satisfactory system performance.