Boosting Cybersecurity Effectiveness through Machine Learning for Proactive Detection and Mitigation of New Threats
Navaneetha Krishna Bose Duraimutharasan, N.Venkatesvara Rao, N. Poongavanam, K. V. Kanimozhi, Manikandan S P · 2024
Cybersecurity still remains an all-time issue before the technological age as the enterprises continue to deal with the evolving nature of the threats arising every day. Traditional cybersecurity approaches are becoming deficient in the situation of constantly improving, swiftly mutating threats, which leads to the revision and refocus of the current approaches to the preventative reactivity type. For that, machine learning (ML) has come into the arena as a strong tool for improving cybersecurity capacity through the.. p roactive” recognition and the “mitigation” of unknown threats. The focus of this paper is the function of ML in cyber security. Here is considered anomaly detection, malware classification, threat intelligence analysis, and security form adversarial attacks. We conduct literature search, detail a novel research methodology embracing data acquisition, preprocessing, ML model training, and deployment. We present experiments versus conventional security approaches from different ML domains used confirming the superiority in cybersecurity of the presented models. Moreover, resistance to chicanery such as adversarial attacks and the models understandability are discussed. Next, research vector and innovation directions in the ML cybersecurity are highlighted. As a whole, this study provides the example of how ML can strengthen previous alliance strategies to cybersecurity and prevent the risks associated with interchanging cyber threats.