A STUDY TO INVESTIGATE THE ROLE OF MACHINE LEARNING IN CYBERSECURITY AND IDENTIFY THE CAPABILITIES OF MACHINE LEARNING IN CYBERSECURITY BEYOND THREAT DETECTION

Abstract

This research explores the revolutionary effects of ML on cybersecurity, particularly as it pertains to its function beyond the conventional detection of threats. Traditional approaches often fail to counteract increasingly complex cyberattacks. Machine learning has the potential to greatly improve cybersecurity techniques by analyzing large datasets and identifying trends. Threat prediction, anomaly detection, or automated response are just a few areas that can benefit from using ML approaches, which are the focus of this study. It delves into how ML models can look at past assault patterns and find small signs humans would miss to foresee new attacks. The research also delves into the function of ML in real-time reaction systems, which can learn from fresh data in real time and adapt to changing threats. The report emphasizes that ML can improve threat intelligence, automate regular security chores, and optimize resource allocation, in addition to detection and response. Organizations may achieve more proactive and adaptable security postures by incorporating ML into cybersecurity frameworks. Among these methods is the use of ML for behavioral analysis, which sheds light on user actions and highlights any discrepancies that can indicate security holes. The study concludes with a thorough examination of how machine learning may reshape cybersecurity procedures. It highlights how technology can provide unique solutions for prediction, reaction, and total security management, going beyond just threat detection. The results could help direct ML studies and applications in the future, to develop cybersecurity systems that are more robust and flexible.