Cyber Security Risk management with attack detection frameworks using multi connect variational auto-encoder with probabilistic Bayesian networks. (October 2022)

• Record Type: Journal Article
• Authors: Mouti, Samar; Shukla, Surendra Kumar; Althubiti, S.A.; Ahmed, Mohammed Altaf; Alenezi, Fayadh; Arumugam, Mahendran
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Cyber attacks -- Cyber-physical systems -- Machine learning -- Security risk management -- Information security management system; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108308
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Detecting cyber-attacks as well as cybercriminals against CPSs (cyber-physical systems) is becoming increasingly difficult. This research proposes novel techniques in cyber security risk management and attack detection frameworks using deep learning architectures. Here the risk management of physical networks has been analysed using multi connect variational auto-encoder. Then the cyber-attacks in the network were detected using probabilistic Bayesian networks. Abstract: This research proposes novel techniques in cyber security risk management and attack detection frameworks using deep learning architectures. Here the risk management of physical networks has been analysed using multi connect variational auto-encoder. Risk values were included in an ISMS (information security management system) as well a quantitative risk assessment was undertaken. According to the quantitative analysis, the proposed remedies could lower risk. Then the cyber attacks in the network were detected using probabilistic Bayesian networks. Performance of deep model is compared to that of a traditional ML method, and detection of distributed attacks is compared to that of a centralised system. According to tests, our distributed attack detection system beats centralised DL-based detection systems. For UNBS-NB-15 dataset, proposed MCVAE_PBNN achieved Accuracy of 96%, False Alarm Rate (FAR) of 71%, Sensitivity of 92%, Specificity of 82%, False positive rate (FPR) of 63%, AUC of 75% and KDD99 dataset proposed MCVAE_PBNN achieved Accuracy of 95%, False Alarm Rate (FAR) of 68%, Sensitivity of 92%, Specificity of 84%, False positive rate (FPR) of 61%, AUC of 78%. Graphical abstract: Image, graphical abstract