Multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning

Cyber security in the context of big data has emerged as a critical issue and an upcoming challenge in the research community. The security problems in big data are handled using machine learning algorithms. In recent years, artificial intelligence has been an emerging technology that machine imitates human behaviors. The Intrusion Detection System (IDS) is the major component for the detection of malicious activities or cyber attacks. In intrusion detection, Artificial intelligence engages a major role widely, which is the better way of building and adapting IDS. Nowadays, Neural Network algorithms are promising techniques for framing the novel artificial intelligence, which is used for real-time problems. The proposed cyber security model uses a cyber defense dataset and a secured network is created based on multi-objective criteria. Here, the optimal feature selection is performed on the basis of a multi-objective function, which focuses on the correlation and accuracy of attack detection using a meta-heuristic algorithm called Beetle Swarm Optimization (BSO). Further, the deep learning model called Recurrent Neural Network (RNN) is used for detecting the attack. The multi-objective-based optimal feature selection is helpful for enhancing the performance of cyber security with reduced redundancy and complexity.