Proceedings ArticleDOI
A Machine Learning Approach to Android Malware Detection
Justin Sahs,Latifur Khan +1 more
- pp 141-147
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TLDR
A machine learning-based system for the detection of malware on Android devices that extracts a number of features and trains a One-Class Support Vector Machine in an offline (off-device) manner, in order to leverage the higher computing power of a server or cluster of servers.Abstract:
With the recent emergence of mobile platforms capable of executing increasingly complex software and the rising ubiquity of using mobile platforms in sensitive applications such as banking, there is a rising danger associated with malware targeted at mobile devices. The problem of detecting such malware presents unique challenges due to the limited resources avalible and limited privileges granted to the user, but also presents unique opportunity in the required metadata attached to each application. In this article, we present a machine learning-based system for the detection of malware on Android devices. Our system extracts a number of features and trains a One-Class Support Vector Machine in an offline (off-device) manner, in order to leverage the higher computing power of a server or cluster of servers.read more
Citations
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Journal ArticleDOI
Machine learning aided Android malware classification
TL;DR: This paper presents two machine learning aided approaches for static analysis of Android malware based on permissions and the other is based on source code analysis utilizing a bag-of-words representation model.
Proceedings ArticleDOI
A New Android Malware Detection Approach Using Bayesian Classification
TL;DR: In this paper, the authors present an effective approach to alleviate the increasing sophistication of Android malware to evade detection by traditional signature-based scanners based on Bayesian classification models obtained from static code analysis, which are built from a collection of code and app characteristics that provide indicators of potential malicious activities.
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Motivating the Rules of the Game for Adversarial Example Research
TL;DR: It is argued that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern, and a taxonomy of motivations, constraints, and abilities for more plausible adversaries is established.
Proceedings ArticleDOI
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis
TL;DR: MARVIN is presented, a system that combines static with dynamic analysis and which leverages machine learning techniques to assess the risk associated with unknown Android apps in the form of a malice score and which correctly classifies 98.24% of malicious apps with less than 0.04% false positives.
Journal ArticleDOI
A review on feature selection in mobile malware detection
TL;DR: This paper studied 100 research works published between 2010 and 2014 with the perspective of feature selection in mobile malware detection, and categorizes available features into four groups, namely, static features, dynamic features, hybrid features and applications metadata.
References
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Scikit-learn: Machine Learning in Python
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
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