The increasing sophistication of cyber threats has made it challenging for traditional security systems to detect and respond to attacks in a timely manner. Machine learning (ML) has emerged as a promising approach to enhance cybersecurity threat detection. This paper provides a comprehensive review of the current state of ML-based threat detection techniques, highlighting their strengths, weaknesses, and applications. We discuss the various types of ML algorithms used in threat detection, including supervised, unsupervised, and deep learning approaches. We also examine the datasets and evaluation metrics commonly used to assess the performance of ML-based threat detection systems. Furthermore, we identify the challenges and limitations of current ML-based approaches and propose future research directions.
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