Enhanced Malware Classification Using Convolutional Neural Network with Feature Optimization
Keywords:
Malware Classification, Convolution Neural Network (CNN), DenseNET, AccuracyAbstract
The rapid evolution of cyber threats and the continuous emergence of new malware variants have created significant challenges for conventional malware detection and classification systems. Traditional signature-based approaches are generally effective for previously identified malware but may fail to detect polymorphic, obfuscated, and previously unseen malware. Deep learning, particularly Convolutional Neural Networks (CNNs), provides an effective solution by automatically learning discriminative patterns from malware representations. However, the performance of CNN-based malware classification can be affected by redundant features, high-dimensional data, class imbalance, and computational complexity. Hence, this research work discloses a novel framework for identifying malware in the Android implemented applications with the help of DL (Deep Learning) methodologies such as enhanced CNN based DenseNET 169 and 201. The proposed approach was tested and trained using Malevis and Malimg Dataset. The results obtained from the experimental procedures demonstrate that the proposed deep learning framework outperforms the various traditional methods with an accuracy of 96.09% and 98.40%.
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