INCIDENT RESPONSE AUTOMATION

Abstract

This paper presents a system for automating cybersecurity incident response to address the limitations of traditional manual approaches. The proposed system integrates machine learning models and orchestration workflows to detect, analyze, and respond to security incidents efficiently. It continuously monitors network logs and behavioral patterns, classifies incidents based on severity, and executes automated response actions such as isolating affected systems and blocking malicious IP addresses. The system architecture includes modules for data collection, analysis, response execution, notifications, user interface, and database management. Implemented using Python and related libraries, the system achieved a model accuracy of 98.33% and an F1 Score of 0.983 on a test dataset. Comprehensive testing confirmed the system's reliability and effectiveness in handling various incident scenarios. The results demonstrate that automation significantly improves response speed and consistency while reducing manual intervention