ALGORITHMIC THREAT HUNTING: ENHANCING DETECTION WITH UNSUPERVISED LEARNING TECHNIQUES IN SOC ENVIRONMENTS

Abstract : Algorithmic threat hunting has emerged as a pivotal approach in modern Security Operations Centers (SOCs) to proactively identify and mitigate advanced cyber threats that evade traditional signature-based detection systems. This paper presents an in-depth exploration of unsupervised learning techniques as a means to enhance threat detection capabilities within SOC environments, emphasizing their ability to uncover novel and stealthy attack patterns without reliance on labeled data. By leveraging unsupervised algorithms such as clustering, anomaly detection, and dimensionality reduction, the proposed methodology facilitates the identification of suspicious activities and outliers across large volumes of heterogeneous security data, including network traffic logs, endpoint telemetry, and system event records. The research systematically evaluates several state-of-the-art unsupervised models, including k-means clustering, DBSCAN, Isolation Forest, and autoencoders, assessing their effectiveness in detecting subtle and previously unseen threats that often bypass conventional defenses. The study further integrates feature engineering strategies tailored to security data, enhancing the discriminative power of input features and improving model interpretability for cybersecurity analysts. Experimental results demonstrate that unsupervised learning approaches can significantly augment threat hunting by reducing false positives, uncovering complex attack vectors, and enabling timely incident response in SOC workflows. Additionally, the paper discusses challenges associated with deploying unsupervised models in operational environments, such as data imbalance, concept drift, and scalability, proposing mitigation strategies including continuous model retraining and feedback loops with human analysts. The combination of automated anomaly detection with expert-driven investigation creates a synergistic framework that accelerates threat discovery and strengthens overall cyber defense posture. This research contributes to the growing body of knowledge on intelligent security analytics by showcasing how algorithmic threat hunting empowered by unsupervised learning can transform SOC operations from reactive to proactive, enhancing resilience against sophisticated cyber adversaries. The findings advocate for broader adoption of these advanced analytical techniques to address evolving cybersecurity challenges and support security teams in safeguarding critical infrastructure and digital assets.

Keyword Algorithmic Threat Hunting, Unsupervised Learning, Anomaly Detection, Security Operations Center (SOC), Cyber Threat Detection, Feature Engineering