Journal ArticleDOI
MalPat: Mining Patterns of Malicious and Benign Android Apps via Permission-Related APIs
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TLDR
An automated malware detection system, MalPat, is implemented to fight against malware and assist Android app marketplaces to address unknown malicious apps.Abstract:
The dramatic rise of Android application (app) marketplaces has significantly gained the success of convenience for mobile users. Consequently, with the advantage of numerous Android apps, Android malware seizes the opportunity to steal privacy-sensitive data by pretending to provide functionalities as benign apps do. To distinguish malware from millions of Android apps, researchers have proposed sophisticated static and dynamic analysis tools to automatically detect and classify malicious apps. Most of these tools, however, rely on manual configuration of lists of features based on permissions, sensitive resources, intents, etc., which are difficult to come by. To address this problem, we study real-world Android apps to mine hidden patterns of malware and are able to extract highly sensitive APIs that are widely used in Android malware. We also implement an automated malware detection system, MalPat, to fight against malware and assist Android app marketplaces to address unknown malicious apps. Comprehensive experiments are conducted on our dataset consisting of 31 185 benign apps and 15 336 malware samples. Experimental results show that MalPat is capable of detecting malware with a high $F_1$ score (98.24%) comparing with the state-of-the-art approaches.read more
Citations
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Journal ArticleDOI
A Review of Android Malware Detection Approaches Based on Machine Learning
TL;DR: This paper presents a comprehensive survey of Android malware detection approaches based on machine learning and analyzes the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness.
Journal ArticleDOI
PermPair : Android Malware Detection Using Permission Pairs
TL;DR: An innovative detection model, named PermPair, is proposed that constructs and compares the graphs for malware and normal samples by extracting the permission pairs from the manifest file of an application and an efficient edge elimination algorithm is proposed.
Journal ArticleDOI
A Systematic Literature Review of Android Malware Detection Using Static Analysis
TL;DR: A systematic literature review of the latest work in Android malware detection using static analysis and a preliminary result that neural network model outperforms the non-neural network model inAndroid malware detection is concluded.
Journal ArticleDOI
SEDMDroid: An Enhanced Stacking Ensemble Framework for Android Malware Detection
TL;DR: This work raises a stacking ensemble framework SEDMDroid to identify Android malware that adopts random feature subspaces and bootstrapping samples techniques to generate subset, and runs Principal Component Analysis (PCA) on each subset.
References
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