An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework

• Published in: Neural computing & applications Vol. 35; no. 15; pp. 11459 - 11475
• Main Authors: Sivamohan, S., Sridhar, S. S.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Alliances; Artificial Intelligence; Collaborative learning; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Condition monitoring; Cybersecurity; Data Mining and Knowledge Discovery; Datasets; Decision making; Deep learning; Explainable artificial intelligence; Image Processing and Computer Vision; Industry 4.0; Intrusion detection systems; Krill; Machine learning; Manufacturing; Memory; Neural networks; Optimization; Original Article; Privacy; Probability and Statistics in Computer Science; Theft; Explainable artificial intelligence; Industry 4.0; Krill herd optimization algorithm; Bidirectional long short-term memory; Cyber security

Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources a...