The static analysis and dynamic system-level behavior analysis are common methods used to detect the malicious apps. The innovation of Android source code security detection technology needs to be greatly valued. The new malicious Android applications are also emerging. As a result, the effective detection of malware is very important to mitigate security threats in the Android ecosystem. Hackers also proposed the attacks that can bypass the permission mechanism.
The permission mechanism of Android is coarse-grained, and users are usually ignorant of the sought permissions. Android implements a number of security mechanisms like the permission mechanism to ensure the safety of device resources. Due to the popularity of the Android ecosystem, the malware writers are targeting the Android devices exclusively, and the number of Android malicious apps surged exponentially. Besides, Android powered devices such as cars, fridges, televisions, point of sale (POS) terminals, and ATM booths are expected to flood the user markets within a few years. IntroductionĪmong all smartphone operating systems, Android has occupied over 85% of market share. Our experiments with a data set containing 10,170 apps show that the decisions from the hybrid model can increase the malware detection rate significantly on a real device, which verifies the superiority of this method in the detection of malicious codes.
#SUPER VECTORIZER MALWARE APK#
This method extracts the static features in the core code of the Android application by decompiling APK files, then performs code vectorization processing, and uses the deep learning network for classification and discrimination. The hybrid model only needs to use the sample training model to achieve high accuracy in the identification of the malicious applications, which is more suitable for the detection of the new malicious Android applications than the existing methods. In this paper, a malicious Android application detection method is proposed, which is implemented by the deep network fusion model. Common static detection methods often rely on Hash matching and analysis of viruses, which cannot quickly detect new malicious Android applications and their variants. To combat this serious malware activity, a scalable malware detection approach is needed, which can effectively and efficiently identify the malware apps.
#SUPER VECTORIZER MALWARE FOR ANDROID#
A report indicates that a new malicious app for Android is created every 10 seconds. The malicious APK (Android Application Package) makers use some techniques such as code obfuscation and code encryption to avoid existing detection methods, which poses new challenges for accurate virus detection and makes it more and more difficult to detect the malicious code.
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