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  1. Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics (IWSPA '16)
  2. Acquiring and Analyzing App Metrics for Effective Mobile Malware Detection
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Evaluation of Android Malware Detection Based on System Calls
How Can We Enable Privacy in an Age of Big Data Analytics?
Data Driven Data Center Network Security
Security Analytics in the Context of Adversarial Machine Learning
Acquiring and Analyzing App Metrics for Effective Mobile Malware Detection
Differential Privacy for Collaborative Filtering Recommender Algorithm
Static Analysis of Malicious Java Applets
Countering Phishing from Brands' Vantage Point
Detecting Advanced Persistent Threats using Fractal Dimension based Machine Learning Classification
Clone or Relative?: Understanding the Origins of Similar Android Apps
Towards Automatic Identification of JavaScript-oriented Machine-Based Tracking
Malware Detection Using Dynamic Birthmarks

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Acquiring and Analyzing App Metrics for Effective Mobile Malware Detection

Content Provider ACM Digital Library
Author Medvet, Eric Canfora, Gerardo Mercaldo, Francesco Visaggio, Corrado Aaron
Abstract Android malware is becoming very effective in evading detection techniques, and traditional malware detection techniques are demonstrating their weaknesses. Signature based detection shows at least two drawbacks: first, the detection is possible only after the malware has been identified, and the time needed to produce and distribute the signature provides attackers with window of opportunities for spreading the malware in the wild. For solving this problem, different approaches that try to characterize the malicious behavior through the invoked system and API calls emerged. Unfortunately, several evasion techniques have proven effective to evade detection based on system and API calls. In this paper, we propose an approach for capturing the malicious behavior in terms of device resource consumption (using a thorough set of features), which is much more difficult to camouflage. We describe a procedure, and the corresponding practical setting, for extracting those features with the aim of maximizing their discriminative power. Finally, we describe the promising results we obtained experimenting on more than 2000 applications, on which our approach exhibited an accuracy greater than 99%.
Starting Page 50
Ending Page 57
Page Count 8
File Format PDF
ISBN 9781450340779
DOI 10.1145/2875475.2875481
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-03-11
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Malware android machine learning
Content Type Text
Resource Type Article
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