Reduction of Cyber Value at Risk (CVaR) Through AI Enabled Anomaly Detection

The increasing demand for internet, mobile, and IoT usage has led to a rise in cyber risks, with a high volume of data generated across industries. This poses a challenge in detecting anomalies effectively. Cyber-attacks jeopardize privacy, compliance, and lead to service interruptions, revenue loss, and customer churn. This research explores anomaly detection methods, including supervised, unsupervised, and semi-supervised learning, in the context of prevalent data patterns and the five stages of cyber-attacks. Machine learning plays a crucial role in monitoring traffic, analyzing scan results, detecting, and blocking attacks, and revealing the attacker’ s identity. The focus is on evaluating AI methods for anomaly detection, leveraging the H20.ai tool, known for its potent machine learning algorithms and seamless integration with existing systems. This enhances AI functionality for meaningful insights during cyber-attacks. The paper emphasizes the importance of robust anomaly detection in reducing Cyber Value at Risk (CVaR) and achieving cybersecurity excellence. While anomaly identification is crucial, the paper suggests it should be complemented by encryption, authentication, and incident response mechanisms for a comprehensive cybersecurity strategy. The research opens avenues for future exploration with additional AI tools and algorithms to minimize CVaR.