An Android malware detection method using Dalvik instructions

Zhang, K; Jiang, Qingshan; Zhang, W; Liao, Xuxin

doi:10.1201/b18413-20

Cited by 1 publication

(1 citation statement)

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“…The common static features include API calls, bytecode, permission data, Dalvik, etc. [32,33]. Nevertheless, static behavior methods cannot detect some malicious programs that are executed by downloading malicious code from a regular program.…”

Section: Malware Detection Methodsmentioning

confidence: 99%

A Hierarchical Approach for Android Malware Detection Using Authorization-Sensitive Features

Chen

Jiang

et al. 2021

Electronics

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Android’s openness has made it a favorite for consumers and developers alike, driving strong app consumption growth. Meanwhile, its popularity also attracts attackers’ attention. Android malware is continually raising issues for the user’s privacy and security. Hence, it is of great practical value to develop a scientific and versatile system for Android malware detection. This paper presents a hierarchical approach to design a malware detection system for Android. It extracts four authorization-sensitive features: basic blocks, permissions, Application Programming Interfaces (APIs), and key functions, and layer-by-layer detects malware based on the similar module and the proposed deep learning model Convolutional Neural Network and eXtreme Gradient Boosting (CNNXGB). This detection approach focuses not only on classification but also on the details of the similarities between malware software. We serialize the key function in light of the sequence of API calls and pick up a similar module that captures the global semantics of malware. We propose a new method to convert the basic block into a multichannel picture and use Convolutional Neural Network (CNN) to learn features. We extract permissions and API calls based on their called frequency and train the classification model by XGBoost. A dynamic similar module feature library is created based on the extracted features to assess the sample’s behavior. The model is trained by utilizing 11,327 Android samples collected from Github, Google Play, Fdroid, and VirusShare. Promising experimental results demonstrate a higher accuracy of the proposed approach and its potential to detect Android malware attacks and reduce Android users’ security risks.

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Section: Malware Detection Methodsmentioning

confidence: 99%