INCIDENT RESPONSE OPTIMIZATION: DECISION TREE AND CORRELATION ALGORITHMS IN REAL-TIME CYBER DEFENSE

Abstract : Incident response optimization in real-time cyber defense is critical for minimizing damage and reducing recovery time during security incidents. This paper explores the integration of decision tree and correlation algorithms to enhance the speed, accuracy, and effectiveness of incident response mechanisms. Decision trees are employed due to their interpretability and efficiency in classifying security alerts based on predefined criteria, enabling rapid identification of threat types and prioritization of response actions. Meanwhile, correlation algorithms are utilized to analyze relationships among disparate security events, uncovering hidden patterns and connections that individual alerts may not reveal. By combining these two approaches, the system can not only classify incidents swiftly but also correlate seemingly unrelated events, leading to more comprehensive situational awareness and informed decision-making. This hybrid methodology addresses common challenges in cyber defense such as alert fatigue, false positives, and the complexity of multi-vector attacks by streamlining the triage process and focusing resources on the most critical threats. The study involves the development and deployment of a prototype real-time incident response framework that continuously ingests data from various security sources, including network logs, endpoint monitors, and intrusion detection systems. Decision trees are trained on historical incident data to predict incident categories and response priorities, while correlation algorithms operate in parallel to detect temporal and contextual links across events. Results from simulated attack scenarios demonstrate that the integrated approach significantly reduces mean time to detect (MTTD) and mean time to respond (MTTR), outperforming traditional rule-based systems. Additionally, the approach enhances the precision of incident classification, reducing the volume of false alerts and enabling security teams to focus on genuine threats. The paper discusses implementation challenges such as the need for continuous model retraining to adapt to evolving threat landscapes and the computational overhead associated with real-time correlation processing. It also highlights the importance of maintaining explainability in automated decision-making to support human analysts in validating and adjusting incident response actions. Ultimately, the fusion of decision tree and correlation algorithms in a real-time cyber defense context represents a promising advancement towards smarter, faster, and more reliable incident response systems, capable of dynamically adapting to complex and rapidly changing cyber threats. This work contributes to the ongoing efforts in cybersecurity automation by providing a scalable and practical solution for enhancing defensive capabilities in enterprise environments.

Keyword Incident Response Optimization, Decision Tree Algorithm, Correlation Analysis, Real-Time Cyber Defense, Threat Detection, Alert Correlation