Hybrid intelligent intrusion detection system for multiple Wi‐Fi attacks in wireless networks using stacked restricted Boltzmann machine and deep belief networks

Abstract

SummaryThe recent cutting edge mobile and internet advancements have increased Internet of Things based gadgets more pervasive and cohesive into our day to day lives. There emerges a need to beat the security challenges, prevalently came about because of the uncovered idea of a remote medium, for example a Wi‐Fi network setup. The linked gadgets are ubiquitous, creating high‐dimensional information on an expansive scale. It tends to be seen those illegitimate actions like illegal information access, information stealing, information alteration different other interruption exercises are quickly developing during a decade ago. Wireless network data transfers will be anticipated to surge strongly in the coming years. Feature learning, still, can evade the probable complications, possibly formed by means of large wireless network dimensions. Therefore, the proposed system focuses on developing a Hybrid Intelligent Intrusion Detection System (IDS) for detecting multiple assaults such as Impersonation, Injection, flooding along with other unknown malicious Wi‐Fi attacks using Deep Learning and Machine Learning algorithms. The proposed system is investigated on well referenced Aegean Wi‐Fi Intrusion Dataset (AWID) and the experimental results prove that the utility of deep learning algorithms in feature representation and classification has achieved higher detection accuracy along with lower false alarm rate. To get an optimum feature set and perform dimensionality reduction on Wi‐Fi network data, the proposed framework uses Deep Belief Network (DBN) with stacked layers of Restricted Boltzmann Machine (RBM). In order to identify and categories various Wi‐Fi threats, Deep Neural Networks, and algorithms like SVM, MLKNN, and RAkEL are employed. The anticipated DBN model was given an optimization strategy to enhance classification performance. The optimization strategy involved selecting the best possible feature subset using a feature selection technique, optimizing the DBN model using the best possible hyperparameter values, less reconstruction error values, and utilizing the regularization method to account for weight decay. The experimental findings demonstrate that the use of deep learning algorithms in feature representation and classification has produced improved detection accuracy coupled with a decreased false alarm rate when applied to the proposed hybrid scheme based Intelligent IDS using the well‐referenced AWID dataset.