Industrial control systems (ICSs) are integral parts of smart cities and critical to modern societies. Despite indisputable opportunities introduced by disruptor technologies, they proliferate the cybersecurity threat landscape, which is… Click to show full abstract
Industrial control systems (ICSs) are integral parts of smart cities and critical to modern societies. Despite indisputable opportunities introduced by disruptor technologies, they proliferate the cybersecurity threat landscape, which is increasingly more hostile. The quantum of sensors utilized by ICS aided by artificial intelligence (AI) enables data collection capabilities to facilitate automation, process streamlining, and cost reduction. However, apart from the operational use, the sensors generated data combined with AI can be innovatively utilized to model anomalous behavior as part of layered security to increase resilience to cyberattacks. We introduce a framework to profile anomalous behavior in ICS and derive a cyber-risk score. A novel super learner ensemble for one-class classification is developed, using overlapping rolling windows with stratified,
               
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