A Modified ResNeXt for Android Malware Identification and Classification

Abstract

It is critical to successfully identify, mitigate, and fight against Android malware assaults, since Android malware has long been a significant threat to the security of Android applications. Identifying and categorizing dangerous applications into categories that are similar to one another are especially important in the development of a safe Android app ecosystem. The categorization of malware families may be used to improve the efficiency of the malware detection process as well as to systematically identify malicious trends. In this study, we proposed a modified ResNeXt model by embedding a new regularization technique to improve the classification task. In addition, we present a comprehensive evaluation of the Android malware classification and detection using our modified ResNeXt. The nonintuitive malware’s features are converted into fingerprint images in order to extract the rich information from the input data. In addition, we applied fine-tuned deep learning (DL) based on the convolutional neural network (CNN) on the visualized malware samples to automatically obtain the discriminatory features that separate normal from malicious data. Using DL techniques not only avoids the domain expert costs but also eliminates the frequent need for the feature engineering methods. Furthermore, we evaluated the effectiveness of the modified ResNeXt model in the classification process by testing a total of fifteen different combinations of the Android malware image sections on the Drebin dataset. In this study, we only use grayscale malware images from a modified ResNeXt to analyze the malware samples. The experimental results show that the modified ResNeXt successfully achieved an accuracy of 98.25% using Android certificates only. Furthermore, we undertook extensive trials on the dataset in order to confirm the efficacy of our methodology, and we compared our approach with several existing methods. Finally, this article reveals the evaluation of different models and a much more precise option for malware identification.

Retracted: Visualisation‐based binary classification of android malware using vgg16

“…We used the DREBIN dataset [18] for this experiment. It contains 5560 sample files from 179 distinct malware families.…”

“…In Ref. [18], the authors also used the visualisation-based approach and converted the malware executable file into a digital image for the further process.…”

Convolutional neural networks in automatic control systems: The state-of-the-art

“…Slika 5 ilustruje grupnu konvoluciju, gde simboli H, W i D označavaju visinu, širinu i dubinu, a indeksi 1, 2 i F se odnose na ulazni podatak, izlaznu aktivacionu mapu i filtar, sledstveno. Grupna konvolucija implementirana je u ResNeXt [25] arhitekturu namenjenu za klasifikaciju slika i zadatke prepoznavanja, ali je svoju primenu našla i u identifikaciji i klasifikaciji zlonamernih softvera [26].…”