Exploring Machine Learning Algorithms for Robust Cyber Threat Detection and Classification: A Comprehensive Evaluation

In response to the escalating frequency and complexity of cyber threats, the imperative need to enhance cybersecurity measures is evident. This study explores the potential of machine learning (ML) algorithms in advancing threat detection and classification by automating the identification of security incidents. The abstract presents a thorough assessment of various ML algorithms, including decision trees, support vector machines, and neural networks, for their efficacy in detecting and categorizing cyber threats. The evaluation encompasses a diverse dataset featuring different cyber-attack scenarios and incorporates multiple features such as network traffic patterns, system logs, and user behavior. Performance metrics, such as training accuracy and testing accuracy, are employed to assess the effectiveness of each algorithm. Furthermore, the study investigates the impact of feature selection techniques and model optimization strategies on algorithm performance. The results underscore the capability of ML algorithms to accurately identify and categorize cyber threats, providing valuable insights into their strengths and limitations. This research contributes to the field of cybersecurity by facilitating the development of practical and robust ML-based solutions, ultimately reinforcing cyber defence mechanisms against evolving threats.