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@article{184652,
        author = {Dr. Kismat Chhillar and Dr. Deepak Tomar and Dr. Dharamdas Kumhar},
        title = {Comparative Study of Supervised, Unsupervised and Reinforcement Learning Approaches for Malware Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {2793-2801},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184652},
        abstract = {The rise of malware is an ongoing challenge that jeopardizes the integrity, confidentiality, and availability of computer systems and networks around the globe. To combat the increasingly sophisticated and fast-changing nature of malware, machine learning has become an essential tool, allowing systems to adapt and recognize new threats that go beyond the limits of traditional signature-based detection. This study delves into the effectiveness of various machine learning techniques—supervised, unsupervised, and reinforcement learning—in detecting malware, examining both their technical foundations and real-world applications. Through comparative experiments and a review of recent advancements, the research sheds light on the strengths and weaknesses of each approach. The paper also identifies common datasets and evaluation frameworks used in the field, ensuring a fair comparison among the three learning paradigms. In conclusion, this comparative study offers a critical evaluation of cutting-edge methodologies, pointing out subtle insights and unresolved issues within each approach, while providing recommendations for the best selection and combination of machine learning methods to create robust, scalable, and future-ready malware detection systems.},
        keywords = {Machine Learning, Malware Detection, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep learning, Malware, Cybersecurity.},
        month = {September},
        }