Detecting system for Android malicious code based on deep learning and method thereof

Abstract

The invention discloses a detecting system for an Android malicious code based on deep learning. The detecting system comprises a feature extracting module, a deep learning module and a report generating module, wherein the feature extracting module is used for taking APK (Android Package) procedure as input, combining the static extraction with the dynamic extraction and outputting 0 and 1 for forming an APK procedure feature vector; the deep learning module is used for taking a MLP (Modular Longitudinal Platform) model as a learning model, on the one hand, training and learning a sample set formed by the feature vector and a supervised value, thereby acquiring a mature learning model; on the other hand, the deep learning module is used for taking the feature vector as input, using the mature learning model for outputting a result probability and taking the result probability as the security level of the APK procedure; the report generating module is used for explaining and analyzing for lastly generating an assessment report according to the feature vector and security level of the APK procedure. According to the detecting system provided by the invention, the deep learning module is combined with the detection for the malicious code while the static feature is combined with the dynamic feature, so that the discriminating capability and detecting accuracy of the system for the unknown malicious code are promoted.

Description

technical field [0001] The invention relates to the field of Android malicious code detection, in particular to a deep learning-based Android malicious code detection system and method thereof. Background technique [0002] JuniperNetworks' latest annual security report shows that while the mobile industry is unclear whether there should be two, three or four smartphone operating systems on the market, mobile malware writers have chosen their targets and are swarming to android. [0003] The Android platform is the "hardest-hit area" of malware. In terms of virus transmission, third-party application stores are still the main channel for virus transmission on smart mobile terminals. Due to the openness of the Android system and its wide market share, 96% of the world's viruses come from Android platform. [0004] It can be seen from a large number of related documents that there are currently two main methods for malware detection: one is a detection method based on signat...

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Problems solved by technology

However, these description methods are either too rough or prone to omissions, which often affect the final classification accuracy.

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