Threat Hunting System for Protecting Critical Infrastructures Using a Machine Learning Approach

Mario Aragonés Lozano (), Israel Pérez Llopis and Manuel Esteve Domingo
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Mario Aragonés Lozano: Communications Department, Universitat Politècnica de València, 46022 Valencia, Spain
Israel Pérez Llopis: Communications Department, Universitat Politècnica de València, 46022 Valencia, Spain
Manuel Esteve Domingo: Communications Department, Universitat Politècnica de València, 46022 Valencia, Spain

Mathematics, 2023, vol. 11, issue 16, 1-18

Abstract: Cyberattacks are increasing in number and diversity in nature daily, and the tendency for them is to escalate dramatically in the forseeable future, with critical infrastructures (CI) assets and networks not being an exception to this trend. As time goes by, cyberattacks are more complex than before and unknown until they spawn, being very difficult to detect and remediate. To be reactive against those cyberattacks, usually defined as zero-day attacks, cyber-security specialists known as threat hunters must be in organizations’ security departments. All the data generated by the organization’s users must be processed by those threat hunters (which are mainly benign and repetitive and follow predictable patterns) in short periods to detect unusual behaviors. The application of artificial intelligence, specifically machine learning (ML) techniques (for instance NLP, C-RNN-GAN, or GNN), can remarkably impact the real-time analysis of those data and help to discriminate between harmless data and malicious data, but not every technique is helpful in every circumstance; as a consequence, those specialists must know which techniques fit the best at every specific moment. The main goal of the present work is to design a distributed and scalable system for threat hunting based on ML, and with a special focus on critical infrastructure needs and characteristics.

Keywords: critical infrastructure protection; threat hunting; cyberattacks; artificial intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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