A Binary Firefly Algorithm Based Feature Selection Method on High Dimensional Intrusion Detection Data

Abstract

Network intrusion detection system are significant features that contribute to the enterprises and organization network success. In the past decade, Intrusion Detection system (IDS) using several methods have been proposed and implemented to safeguard the networks within an organization to be reliable, secure, and readily available. Although, some of these methods are built utilizing machine learning concepts. IDS utilizing machine learning techniques are accurate, effective and efficient in spotting attacks. However, the machine learning model performance reduces with high dimensional intrusion dataset. Therefore, it is sacrosanct to implement an efficient feature selection technique that can generate a great impact on the classification stage. Also, the feature selection phase is the most serious phase in IDS based on machine learning. This phase is expensive both in time and efforts. Furthermore, most of the proposed machine learning intrusion detection system suffered from low detection accuracy and high false positive rate when the models are experimented on high dimensional dataset. The aim of this paper is to propose a binary firefly algorithm (BFFA) based feature selection for IDS. We first performed normalization in the first stage of the model. Subsequently, the BFFA algorithm was used for feature selection stage. We adopted random forest algorithm for the classification phase. The experiment was performed on high dimensional University of New South Wales-NB 2015 (UNSW-NB15) dataset with seventy five percent of the data used for training the model and twenty percent for testing. The findings showed an accuracy of 99.72%, detection rate of 99.84%, precision of 99.27%, recall of 99.84% and F-score of 99.56%. The results were gauge with the state-of-the-art results and our results were found outstanding.