Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment

• Published in: Cognitive neurodynamics Vol. 16; no. 5; pp. 1045 - 1057
• Main Authors: Alohali, Manal Abdullah, Al-Wesabi, Fahd N., Hilal, Anwer Mustafa, Goel, Shalini, Gupta, Deepak, Khanna, Ashish
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-10-2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Belief networks; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Cognitive ability; Cognitive Psychology; Computation; Computer Science; Cyber-physical systems; Cybersecurity; Data conversion; Decision making; Design; Industrial applications; Industry 4.0; Informatics; Intrusion detection systems; Long short-term memory; Machine learning; Manufacturing; Neural networks; Neurosciences; Recurrent neural networks; Research Article; Security; Cognitive computing; Intrusion detection system; Artificial intelligence; Industry 4.0. cognitive cyber physical system
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-022-09780-8

In recent days, Cognitive Cyber-Physical System (CCPS) has gained significant interest among interdisciplinary researchers which integrates machine learning (ML) and artificial intelligence (AI) techniques. This era is witnessing a rapid transformation in digital technology and AI where brain-inspired computing-based solutions will play a vital role in industrial informatics. The application of CCPS with brain-inspired computing in Industry 4.0 will create a significant impact on industrial evolution. Though the CCPSs in industrial environment offer several merits, security remains a challenging design issue. The rise of artificial intelligence AI techniques helps to address cybersecurity issues related to CCPS in industry 4.0 environment. With this motivation, this paper presents a new AI-enabled multimodal fusion-based intrusion detection system (AIMMF-IDS) for CCPS in industry 4.0 environment. The proposed model initially performs the data pre-processing technique in two ways namely data conversion and data normalization. In addition, improved fish swarm optimization based feature selection (IFSO-FS) technique is used for the appropriate selection of features. The IFSO technique is derived by the use of Levy Flight (LF) concept into the searching mechanism of the conventional FSO algorithm to avoid the local optima problem. Since the single modality is not adequate to accomplish enhanced detection performance, in this paper, a weighted voting based ensemble model is employed for the multimodal fusion process using recurrent neural network (RNN), bi-directional long short term memory (Bi-LSTM), and deep belief network (DBN), depicts the novelty of the work. The simulation analysis of the presented model highlighted the improved performance over the recent state of art techniques interms of different measures.