The Role of Machine Learning in Cybersecurity

In recent years, the prevalence of cyber threats has escalated significantly, necessitating innovative methods for safeguarding digital assets. This paper explores the integration of machine learning (ML) techniques in cybersecurity, focusing on anomaly detection, malware classification, and threat intelligence. We begin by reviewing the fundamental concepts of machine learning and its applicability to cybersecurity challenges. The study highlights the advantages of using ML algorithms for predictive analytics in identifying potential threats before they manifest. We further discuss the limitations and challenges faced in implementing machine learning solutions, including issues of data quality, model interpretability, and adversarial attacks. By analyzing various case studies, we illustrate the effectiveness of ML in enhancing the security posture of organizations. The paper concludes with recommendations for future research directions and practical implications in the field of cybersecurity

کلیدواژه ها:

Cyber threats ، Protection of digital assets ، Machine learning (ML)