Deep Convolution Neural Networks and Image Processing for Malware Detection
Amel Ksibi, Mohammed Zakariah, Latifah Almuqren, Ala Saleh Alluhaidan · Research Square · 2023
Abstract Current anti-malware technologies have exposed its glaring vulnerabilities as a result of a signature-based approach as more sophisticated malware has been appearing in recent years, particularly in the android operating system. The state-of-the-art literature offers a wide range of possibilities, but none of them are flawless in terms of providing clear and timely solutions. The current study used a CNN-based deep learning architecture to address this problem. The proposed method collected RGB images from unprocessed malware binaries. We explored complex high-level aspects that effectively identify malware families using an image-based method rather than feature representations in order to detect and identify malware families. The RGB graphics were extracted from the raw APK files because colour images may hold more data in the source code. We developed deep CNNs using produced images that extract higher-level semantics associated with malware. This has allowed us to develop more complex software with enhanced security and made it more challenging for attackers to avoid detection. We trained a customised CNN and a refined Vgg-16 architecture using their covered images. The Android malware datasets CIC-AndMal2017 and CICMalDroid 2020 were used in this analysis. Results show that the shallow CNN model is defeated by the pretrained vgg-16. The accuracy of the suggested method, which is substantially better than earlier studies on the topic, has been assessed to detect malware samples with a 97.81% accuracy rate.