Malware Classification on Malimg Using MobileNet and LSTM for Efficient Detection

Malware detection is essential for protecting systems from evolving threats, with traditional methods struggling to keep up with increasingly sophisticated malware. In this study, we present a hybrid model combining MobileNet's efficiency with LSTM's sequential learning to enhance malware classification. Trained on the Malimg dataset, the model achieved a high test accuracy of 99.26%, surpassing existing approaches. The results demonstrate its effectiveness and suitability for deployment in resource-constrained environments, offering a promising solution for efficient malware detection.