This research paper presents an AI-powered cyber attack detection system using machine learning techniques for modern cybersecurity defense. The study explores intrusion detection systems, anomaly detection, phishing detection, malware detection, ransomware detection, and network traffic analysis using machine learning algorithms such as Random Forest, Decision Tree, SVM, XGBoost, and Neural Networks. The paper discusses system architecture, preprocessing techniques, dataset analysis, training methodologies, experimental evaluation, and future research directions in AI-driven cybersecurity. The proposed framework focuses on scalable and real-time cyber threat detection for modern enterprise environments.
Syed Arsalan Ahmed (Fri,) studied this question.
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