Hybrid Approaches Combining Machine Learning and Heuristics for Advanced Persistent Threat Detection

Abstract

Advanced Persistent Threats (APTs) represent a growing concern for organizations worldwide, as these threats are characterized by their stealth, sophistication, and prolonged nature. Traditional cybersecurity measures often fall short in effectively detecting and mitigating APTs due to their reliance on signature-based detection methods. This research proposes a hybrid cybersecurity model that combines machine learning algorithms with heuristic analysis to enhance the detection of APTs. The model leverages the strengths of both methodologies, where machine learning algorithms analyze vast datasets to identify patterns and anomalies, while heuristic techniques provide rules and guidelines based on known threat behaviors. This paper discusses the architecture of the proposed model, its implementation, and evaluates its performance against traditional detection methods. The results indicate a significant improvement in detection accuracy and a reduction in false positive rates, underscoring the potential of hybrid approaches in advancing cybersecurity defenses.