A novel and secure artificial intelligence enabled zero trust intrusion detection in industrial internet of things architecture

作者：Asif Ali Laghari, Abdullah Ayub Khan, Amel Ksibi, Fahima Hajjej, Natalia Kryvinska, Ahmad Almadhor, Mohamad Afendee Mohamed, Shtwai Alsubai · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-11738-9 · 被引用次数：48 · 研究领域：Smart Grid Security and Resilience、Anomaly Detection Techniques and Applications、Network Security and Intrusion Detection

Some of the potential advancements in information and communication technology (ICT), like artificial intelligence (AI) with federated learning (FL), which are particularly used in the transformation of industrial environments, are driven by digital technologies and can increase the efficiency and dependability of systems. The world has recently become more interconnected as a result of digital technology adoption but with some significant drawbacks. One of the major problems that arises as a malevolent possibility is cybercrime, which poses a threat to governments, corporations, and societies-most significantly, civil society. It is now feasible to work with more than two people to integrate Advanced Digital Technology (ADT) and move the foundation in the direction of a strong hierarchy, but in a complicated way, which has turned into a playground for cybercriminals. The idea of zero trust offers some sweetness in demand for threat detection by autonomously based FL, ML, and DL to address such difficult elements. This paper's main objective, however, is to offer an investigative report based on cyber vulnerability detection using AI, ML, and DL in order to shield the ecosystem from malevolent attacks before they happen. Examining machine learning-enabled classifiers and ensembles for network intrusions and autonomous malicious detection is the secondary objective. Therefore, it explains how we can combine the two models to analyze the context of attacks more effectively and ...