QUASI OPPOSITIONAL LEARNING BASED AFRICAN BUFFALO OPTIMIZATION FOR ROBUST FEATURE SELECTION IN NETWORK INTRUSION DETECTION SYSTEM

Detecting the anomaly network behavior is complex to establish the secure communication in network or system. The anomaly activities in networks seriously threaten the privacy of data, functions and the whole network infrastructure. The redundant, irrelevant and high-dimension features cause the overfitting issue in the learning process, resulting in high False Positive Rate (FPR) and less classification performance. To eliminate the redundant and high-dimension features, this article developed the Quasi Oppositional Based Learning (QOBL) strategy – African Buffalo Optimization (ABO) algorithm. The QOBL strategy is included in conventional ABO algorithm which improves the search ability and convergence rate for enhancing performance of feature selection process. In the classification phase, Recurrent Neural Network – Long Short-Term Memory (RNN-LSTM) technique is utilized to find intrusions in networks with high accuracy and less FPR. QOBL-ABO and RNN-LSTM based classifier obtained 99.99% accuracy on NSL-KDD and 99.94% on UNSW-NB15 dataset when compared to previous algorithms like Deep Neural Network (DNN).