Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{178828,
        author = {Anshika Gupta and Saloni Gupta and Yana Chauhan},
        title = {Malware Detection using ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8781-8787},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178828},
        abstract = {The increasing sophistication and frequency of malware attacks pose a significant threat to cyber security. This project presents a machine learning-based approach to malware detection that leverages the ability of algorithms to learn patterns from data and generalize to unseen threats. By extracting and analyzing features from both malicious and benign software samples, several classification algorithms—including Random Forest, Support Vector Machine (SVM), and Neural Networks—were trained and evaluated.},
        keywords = {},
        month = {May},
        }