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@article{177727,
        author = {shivani and sarabjot singh walia},
        title = {RESEARCH PAPER ON MALWARE IMAGES CLASSIFICATION USING DEEP LEARNING TECHNIQUES BASED CONVOLUTION NEURAL NETWORKS (CNNs)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1285-1289},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177727},
        abstract = {Malware is designed to damage computers or computer networks. Malware is a general term used to describe any program designed to harm a computer. The thing is to commit a crime, analogous as gaining unauthorized access to a particular system, so as to compromise user security. utmost malware still uses the same law to produce another different form of malware variants. therefore, the capability to classify similar malware variant characteristics into malware families is a good strategy to stop malware The exploration is useful for classifying malware on malware samples presented as byte chart grayscale images. For malware discovery, DL algorithm like modified VGG is used with an image- grounded malware dataset. For experimental setting, the proposed model Mal Net successfully linked malware images. Mal Net was also used to identify malware images and compared it to other trained models. The suggested system produced accurate and precise results.},
        keywords = {Malware Images Bracket, DL ways, VGG architecture, Cyber Analysis, Mal Net, convolution Neural Network.},
        month = {May},
        }