<scp>DSecNet</scp> : An Enhanced Cyber Security System for Authentication, Intrusion Detection, and Risk Level Prediction in Information Systems Using Weighted <scp>RBM</scp> Features‐Based <scp>DeepCapsNet</scp>

ABSTRACT The traditional cybersecurity solution cannot offer the necessary protection against all forms of cyberattacks for management systems since cyberattacks are of a scattered nature. Thus, an efficient security method is suggested in this work to prevent data loss from the information system using optimization and deep learning. The major aim of this work is to offer suitable recommendations for developing a cybersecurity decision support model for information systems with risk analysis and cybersecurity models. In the information system, the security threats are eliminated using this model. So, the authentication, prediction of the risk levels, and the mitigation of the security threats are regarded as the major objectives of this research work. From the CICIDS 2017 KNN dataset, UNSW‐NB15 dataset, and NSL‐KDD dataset, the data is collected. The collected data is given to the executed Deep Security Network (DSecNet). In the DSecNet, the Restricted Boltzmann Machine (RBM) is utilized to retrieve the features and then the extracted features from the RBM are fused with optimized weights, which are selected using the Enhanced Sandpiper Optimization Algorithm (ESOA). The obtained weighted features are further provided to the Deep Capsule Neural Network (DeepCapsNet). The implemented DSecNet is used for verifying the authentication of the user, detecting the intrusion, and determining the associated risk levels caused by the attacks on the information system. Once the threats are detected, the mitigation of the threat based on their risk level is carried out. The effectiveness of the proposed model is proven by the validation process. From the simulation outcomes, the accuracy rate of the developed model is 97.11% in the NSL‐KDD dataset and 96.70% in the UNSW‐NB15 dataset based on intrusion detection performance. Therefore, the efficacy of the developed security system is higher for the performance of the authentication, intrusion detection, and risk prediction, which elevates the security of the cyber networks.