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@article{176051,
        author = {Varsharani A. Pawar and Dr. S. B. Chaudhari},
        title = {Advanced Image-Based Malware Detection Leveraging Sparse CNN Networks for Intelligent Feature Recognition and Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4875-4881},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176051},
        abstract = {The proliferation of sophisticated malware has posed significant challenges to traditional detection mechanisms, which often fail to keep pace with rapidly evolving threats. In response, this project presents an advanced image-based malware detection system leveraging Sparse Convolutional Neural Networks (Sparse CNNs) for intelligent feature recognition and classification. The proposed approach transforms malware binary files into grayscale images, enabling the application of computer vision techniques for behavioral pattern analysis. By integrating Sparse CNNs, the system efficiently captures essential structural features within malware images while minimizing computational overhead through sparsity-inducing techniques. This not only enhances detection accuracy but also improves the system's generalization capabilities, making it resilient to unseen and obfuscated malware variants. The model is trained and evaluated on benchmark datasets, with results demonstrating high classification accuracy and robustness compared to traditional CNN-based approaches. This dissertation contributes to the development of intelligent, scalable, and lightweight malware detection solutions that can be effectively deployed in real-world cybersecurity environments.},
        keywords = {Malware Detection, Image-Based Analysis, Sparse Convolutional Neural Network (Sparse CNN), Deep Learning, Feature Extraction, Cybersecurity, Malware Classification, Binary-to-Image Conversion, Pattern Recognition},
        month = {April},
        }