Abstract
With the rapid development of the Internet era, the number of malware has reached an unprecedented peak, and therefore malware is threatening global network security seriously. In this article, we propose an Android malware detection approach based on SIMGRU, which belongs to the static detection approach. The similarity of clustering is widely used in static analysis of android malware, so we introduce the similarity to improve Gated Recurrent Unit (GRU), and obtain three different structures of SimGRU: InputSimGRU, HiddenSimGRU, and InputHiddenSimGRU. The InputHiddenSimGRU is the combination of InputSimGRU and HiddenSimGRU. The experiment shows that InputSimGRU, HiddenSimGRU, and InputHiddenSimGRU outperform the conventional GRU model and other methods.
Highlights
Nowadays, a large number of mobile software is released constantly into the application market, which provides favorable conditions for malicious software spread
This article presents an Android malware detection approach based on SimGRU, which belongs to the static malware detection
SIMGRU We introduce the principle of similarity to Gated Recurrent Unit (GRU) to improve the performance of android malware detection due to the similarity function that other works utilize to detect malware
Summary
A large number of mobile software is released constantly into the application market, which provides favorable conditions for malicious software spread. Experts proposed many approaches to identify malware [2]–[9] They can be classified as static detection, dynamic detection, and hybrid detection. Talha et al [4] presented a permission-based Android malware detection system that applies static analysis to classify Android applications as benign or malicious. Allix et al [7] devised several machine learning classifiers that rely on a set of features built from applications’ CFGs. Wu et al [8] proposed an Android malware detecting system that provides accurate classification and sensitive data analysis. This article presents an Android malware detection approach based on SimGRU, which belongs to the static malware detection. Due to the wide applicability of the similarity in the static malware detection, we introduce the similarity principle to the GRU cell and establish a new Android malware detection model-SimGRU.
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