Cyber Attacks Classification Using Supervised Machine Learning Techniques

Abstract

Cyberattack classification through the utilization of supervised machine learning methods. The system is designed to categorize diverse cyber-attacks by employing a meticulously curated dataset encompassing a wide array of attack types, including but not limited to malware, phishing, and distributed denial-of-service (DDoS) attacks. Feature extraction techniques are applied to both network traffic data and behavioural attributes, facilitating the training of a robust classification model. Various supervised learning algorithms, such as decision trees, support vector machines, and neural networks, are evaluated for their efficacy in accurately predicting attack categories. The training process involves labelling historical attack instances, enabling the model to discern intricate patterns and subtle differentiators among attack types. Regular model updates and retraining with new attack data ensure its relevance in dynamically evolvingthreat landscapes. The system's predictive accuracy empowers cybersecurity teams to swiftly identify and respond to cyber threats, thereby bolstering overall defence strategies. Through this research, we contribute to the proactive identification and mitigation of cyber-attacks, ultimately fortifying digital security frameworks.