Network Traffic Cyber Attacks Classification using Supervised Machine Learning Techniques

Cyber attack classification through the utilization of supervised machine learning methods is investigated in this study. 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 restr to malware, phishing, and (DDoS) attacks. Feature extraction techniques are applied to both network traffic data and behavioral attributes, facilitating the training of a robust classification model. Various supervised learning algorithms, such as the Adaboost classifier, Catboost classifier, and Gaussian Naïve Bayes, 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 evolving threat landscapes. The system's predictive accuracy empowers cyber security teams to swiftly identify and respond to cyber threats, thereby bolstering overall defence strategies. Through this research, I contribute to the proactive identification and mitigation of cyber-attacks, ultimately fortifying digital security frameworks.