Network Traffic Analysis for Cyber Attack Classification Using Supervised Learning Models

With the increasing reliance on digital infrastructure, the risk of cyber-attacks has grown exponentially. Cyber-attacks such as phishing, malware, denial-of-service (DoS), and advanced persistent threats (APTs) can have devastating consequences for organizations and individuals. This project presents a comprehensive approach to classifying cyber-attacks using supervised machine learning techniques. By leveraging labelled datasets, machine learning models are trained to identify and classify various types of cyber-attacks based on network traffic, system logs, and user behavior patterns. The proposed system aims to enhance the efficiency of intrusion detection systems (IDS) by automating the detection and classification process, ensuring real-time protection against diverse threats. This research highlights the importance of data pre-processing, feature selection, and hyperparameter optimization in achieving high accuracy and precision in cyber-attack classification.