Threat Hunting Architecture Using a Machine Learning Approach for Critical Infrastructures Protection

Abstract

The number and the diversity in nature of daily cyber-attacks have increased in the last few years, and trends show that both will grow exponentially in the near future. Critical Infrastructures (CI) operators are not excluded from these issues; therefore, CIs’ Security Departments must have their own group of IT specialists to prevent and respond to cyber-attacks. To introduce more challenges in the existing cyber security landscape, many attacks are unknown until they spawn, even a long time after their initial actions, posing increasing difficulties on their detection and remediation. To be reactive against those cyber-attacks, usually defined as zero-day attacks, organizations must have Threat Hunters at their security departments that must be aware of unusual behaviors and Modus Operandi. Threat Hunters must face vast amounts of data (mainly benign and repetitive, and following predictable patterns) in short periods to detect any anomaly, with the associated cognitive overwhelming. The application of Artificial Intelligence, specifically Machine Learning (ML) techniques, can remarkably impact the real-time analysis of those data. Not only that, but providing the specialists with useful visualizations can significantly increase the Threat Hunters’ understanding of the issues that they are facing. Both of these can help to discriminate between harmless data and malicious data, alleviating analysts from the above-mentioned overload and providing means to enhance their Cyber Situational Awareness (CSA). This work aims to design a system architecture that helps Threat Hunters, using a Machine Learning approach and applying state-of-the-art visualization techniques in order to protect Critical Infrastructures based on a distributed, scalable and online configurable framework of interconnected modular components.

Integration of Machine Learning and Human Expertise for the Improvement of Cybersecurity: Applications and Challenges

“…The complexities at the intersection of AI and cybersecurity include promising opportunities for enhanced security measures and the challenges posed by adversarial threats [2]. This dual nature is further explored through a threat-hunting architecture using ML to protect critical infrastructures [3]. The importance of AI in enhancing threat detection and mitigation capabilities is underscored, stressing the need for continuous improvement in AI algorithms to stay ahead of cyber threats [4].…”