Optimal cluster based feature selection for intrusion detection system in web and cloud computing environment using hybrid teacher learning optimization enables deep recurrent neural network

Abstract

Information technology organizations have experienced rapid growth in recent years, resulting in scalability, mobility, and flexibility challenges. Those organizations move their data to the cloud because security and privacy are major concerns. As cloud computing becomes more popular, security has become an important concern. These confidential data are vulnerable to attacks/malicious or intruders due to the characteristics of the cloud. In order to address the growing concern of real-time intruders, a variety of intrusion detection systems (IDS) used specifically for cloud environments with the aim of enhancing overall security. There are, however, some limitations and known attacks that can be overcome by those IDSs. We recently proposed a hybrid soft computing based IDS (ST-IDS) for web and cloud environments, but missed some novel web and cloud attacks. Using hybrid teacher learning enabled deep recurrent neural networks and cluster based feature optimization, we propose an IDS scheme for web and cloud computing environments. MMFO (modified manta-ray foraging optimization) is used after feature extraction to select optimal features for further detection. To classify the intrusion in the web-cloud environment, a hybrid teacher-learning enabled deep recurrent neural network (TL-DRNN) is introduced. Our proposed IDS scheme has been validated using benchmark datasets including DARPA LLS DDoS-1.0, CICIDS-2017, and CSIC-2010. The performance of our proposed IDS scheme has been compared to existing IDS schemes using various quality measures.