A Machine Learning–Driven Framework for Intelligent Cyberattack Detection and Classification

The rapid development of cloud platforms, data-centric applications, and networked systems has significantly increased the complexity and frequency of cyber threats. Cyberattacks nowadays are very flexible and frequently evade conventional defences. Conventional intrusion detection systems (IDS) are useless against new and emerging threats because they rely on signature-based techniques and static rules. To identify malicious activities and classify them into distinct attack types, the proposed system analyses network traffic patterns. For intrusion detection, a variety of supervised machine learning algorithms are trained and assessed using benchmark datasets. According to experimental results, the suggested framework achieves high detection accuracy, low false-positive rates, and enhanced adaptability, making it ideal for real-time cybersecurity applications.