My current research focuses on developing an AI-driven automated threat detection and incident-response framework that can detect and react to cybersecurity threats in real time. The system integrates machine learning algorithms with cybersecurity automation pipelines to improve detection speed, precision, and adaptability. It applies boosting, anomaly detection, and ensemble models to analyze behavioral patterns in large-scale datasets. The framework also features a rule-based SOC module that autonomously correlates alerts, prioritizes threats, and executes mitigation steps, minimizing manual effort and response time. It bridges the gap between human-driven analysis and intelligent automation, creating a scalable and self-learning security system that enhances cyber resilience.

Get in Touch...