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
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-08319-0

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 and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using data cleaning and normalization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to improve interpretation of prediction results. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%.