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@article{192571,
        author = {Adwaitha Jampelly and Karthik Kandi and G V Surya Shashank and Nithya Chadalavada},
        title = {Machine Learning in Cybersecurity: Threat Detection and Prevention},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1498-1504},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192571},
        abstract = {The rapid growth of digital technologies and internet- based services has greatly increased the exposure of both individuals and organizations to cyber threats. Traditional cybersecurity tools, such as firewalls, antivirus software, and rule-based intrusion detection systems, depend on predefined signatures and fixed rules. While these methods are useful for identifying known attacks, they often struggle to keep up with increasingly complex and evolving cyber threats. Machine learning has emerged as a promising solution to these challenges by allowing security systems to learn directly from data, recognize malicious patterns, and adapt to new forms of attacks. This review paper examines the role of machine learning in cybersecurity, with a particular focus on threat detection and prevention. It explores a range of machine learn- ing techniques, including supervised learning, unsupervised learning, and deep learning, and discusses their applications in areas such as intrusion detection, malware classification, phishing detection, and fraud detection. In addition, the paper addresses key challenges associated with applying machine learning in cybersecurity, including data quality limitations, class imbalance, adversarial attacks, and privacy concerns. Finally, it outlines future research directions, highlighting the growing need for explainable, adaptive, and secure machine learning-based cybersecurity systems.},
        keywords = {Machine Learning, Cyber Security, Threat Detection, Intru- sion Detection Systems, Malware Classification, Phishing De- tection, Fraud Prevention, Deep Learning, Supervised Learn- ing, Unsupervised Learning, Adversarial Attacks, Network Security},
        month = {February},
        }