Machine Learning Techniques in Cybersecurity Threat Detection: A Systematic Literature Review

There is a systematic analysis of machine learning techniques for cybersecurity threat detection provided in this study. It contrasts conventional machine learning, deep learning, and ensemble methods in the most prominent categories of cyber threats, and reveals important challenges like adversarial attacks, imbalanced data sets, model interpretability, concept drift, and limited ability to generalize. Another key finding of the study is that it identifies several research gaps and opportunities that would guide future research in more adaptive, explainable, and region-specific cybersecurity solutions.