Anomaly detection in heterogeneous cybersecurity data

The increasing complexity of cybersecurity threats necessitates advanced methods to detect anomalies across diverse and heterogeneous data sources. Traditional security systems often struggle with isolated data silos, high false-positive rates, and the inability to adapt to evolving threats. Anomaly detection has emerged as a critical approach to addressing these challenges by identifying deviations from expected behaviours that may indicate malicious activities. This paper explores the application of anomaly detection techniques in heterogeneous cybersecurity data, encompassing network traffic logs, endpoint telemetry, user activity, and external threat intelligence. It examines the role of machine learning, deep learning, and statistical models in processing and correlating these diverse datasets to identify threats with improved accuracy and speed. The discussion includes challenges such as managing data diversity, scalability, and balancing sensitivity with specificity in detection. Through a review of case studies and recent advancements, the paper highlights successful implementations of anomaly detection, including hybrid approaches combining unsupervised learning with domain expertise. This work underscores the importance of anomaly detection in safeguarding digital ecosystems against increasingly sophisticated cyber threats.