Binary bat algorithm based feature selection with deep reinforcement learning technique for intrusion detection system

Abstract

The new generation of Intrusion Detection Systems (IDS) progressively requires automatic and intellectual network ID strategies for managing security risks made by a rising amount of advanced attackers from the cyber environment. Specifically, there were huge demands for autonomous agent-related IDS solution which need some human intervention as possible whereas can progress and enhance themselves (by making suitable acts for a presented environment) and turns out to be highly powerful to effective threats that were not seen before (for instance, zero-day attacks). Recently, DRL methods were presented that can learns from the environment with uncontrollable massive amount of states for addressing the major drawbacks of prevailing RL methods. This article introduces a Binary Bat Algorithm-based Feature Selection with Deep Reinforcement Learning (BBAFS-DRL) system for IDSs. The major intention of the BBAFS-DRL approach is the recognition and classification of intrusion systems. In the projected BBAFS-DRL method, data pre-processed was primarily executed to change the data as suitable format. Furthermore, the BBAFS system can be utilized for the effectual feature selection. Next, deep Q-network is applied for the effectual identification and classification of intrusion. At last, the root means square propagation (RMSProp) optimizer is utilized for the effectual hyperparameter tuning procedure. The experimental analysis of the BBAFS-DRL algorithm can be tested by utilize of benchmark database and the outcome can be analyzed on many measures. The comparison outcome demonstrated the improvement of the BBAFS-DRL methodology over other existing approaches.