Android malware detection for timely detection using multi-class deep learning methods
M. Geerthana Anusha, M. Karthika · International Journal of Intelligent Engineering Informatics · 2024
Android malware has emerged as a severe danger to national security because of the widespread usage of smartphones and the inherent risk it provides to its users. Due to code obfuscation, antivirus products and other typical detection algorithms struggle to catch Android malware, which has increased. Deep learning-based solutions safeguard legitimate Android users against fraudulent apps, which is a must. The methods categorise Android malware using multiple feature representations. Unfortunately, as more apps use classifiers, the temptation to weaken them develops. According to current research, deep learning is being used to identify malware. A learning-based classifier processes Android application properties to test deep learning for Android virus detection safety (apps). Considering the features' importance to the classification problem and the costs of changing them, we suggest an encoder-decoder-based CNN feature selection approach to make the classifier tougher to bypass. We also provide a spider monkey optimisation-based Bi-LSTM method that combines classifiers from our feature selection strategy to improve system security without compromising detection accuracy. Testing on CICInv and Mal2021/CICInv sample sets proved the suggested strategy's efficacy against malicious Android malware attacks. In addition, any malware detection setup can employ our secure-learning paradigm.