Machine Learning Techniques for Real-Time Cyber Threat Detection and Prevention

Abstract

Background: India faced a surge of cyber threats, from their frequency to sophistication, as the growth of Digital Technologies, Cloud Computing, Internet of Things (IoT), Artificial Intelligence (AI), and the rise of online financial services. In this regard, machine learning (ML) is a potential solution that offers intelligence, adaptability, and real-time detection and prevention of cyber threats. While the studies on ML applications in the field of cybersecurity have been conducted, a thorough synthesis study on the available evidence on cybersecurity in the Indian context has yet to be carried out.
Objective: This systematic review will focus on evaluating the literature related to machine learning techniques for real-time cyber threat detection and prevention in India.
Methods: This study used a systematic review design and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed. A thorough literature search was performed in all major electronic databases such as IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, ACM Digital Library and Google Scholar. The articles were included if they were published in English and reviewed by peers from 2020-2026.
Conclusion: Machine learning has proven to be a transformative technology for enhancing cybersecurity, with its ability to detect threats intelligently, adaptively, and automatically. Despite the progress made, there is still a need for more research and development to build easily explainable, scalable, and context-specific machine learning models that can adapt to India's growing cybersecurity challenges. The insights from this review offer tangible support for researchers, practitioners, and policymakers in their quest for enhanced AI-driven cybersecurity solutions and a more secure digital landscape.