Leveraging Machine Learning to Improve Cybersecurity: Methods, Obstacles, and Prospects

This study intends to investigate how machine learning (ML) can be applied to improve cybersecurity measures, with a particular focus on systems for anomaly, fraud, malware, and intrusion detection. A total of ۷۴۳ academic papers were systematically reviewed; ۱۱۵ of these were chosen for further examination. Advances in machine learning techniques were assessed in the context of cybersecurity as part of the review process. The results show that machine learning (ML)-driven systems greatly increase the automation of security procedures, enhance the detection of new threats, and lower human error in cyber threat management. But obstacles like hostile attacks and the requirement for excellent model training stand in the way of the wider use of ML in cybersecurity. The paper addresses the ramifications of these results, highlighting the need to create resilient and flexible machine learning models that can resist hostile attacks and enhance integration across a range of cybersecurity applications. The knowledge gathered from this study highlights the necessity of ongoing innovation in threat detection systems and offers a thorough summary of the possible advantages and difficulties of applying machine learning to cybersecurity. The majority of the literature in this review is from Western contexts, which may cause it to miss insights from other regions. Additionally, one of the biggest obstacles still facing ML systems is their complexity when implemented in dynamic cyber environments. Future studies should focus on improving cybersecurity by integrating emerging technologies, improving ML algorithms to make them more resilient to adversarial threats, and filling in the gaps in the literature about the life cycle of ML models in practical applications.

Keywords:

Cyber threat detection , Cybersecurity , Data security , Information security , Machine learning , Malware detection