A DIDS Based on The Combination of Cuttlefish Algorithm and Decision Tree

Abstract

Different Distributed Intrusion Detection Systems (DIDS) based on mobile agents have been proposed in recent years to protect computer systems from intruders. Since intrusion detection systems deal with a large amount of data, keeping the best quality of features is an important task in these systems. In this paper, a novel DIDS based on the combination of Cuttlefish Optimization Algorithm (CFA) and Decision Tree (DT) is proposed. The proposed system uses an agent called Rule and Feature Generator Agent (RFGA) to generate a subset of features with corresponding rules. RFGA agent uses CFA to search for optimal subset of features, while DT is used as a measurement on the selected features. The proposed model is tested on the KDD Cup 99 dataset. The obtained results show that the proposed system gives a better performance even with a small subset of 5 features when compared with using all 41 features.

Keywords

• Feature Selection Distributed Intrusion Detection System
• Cuttlefish Optimization
• Mobile agent