DroidAnalytics: A Signature Based Analytic System to Collect, Extract, Analyze and Associate Android Malware
Not on the map. Why?
Abstract (arXiv)
Smartphones and mobile devices are rapidly becoming indispensable devices for many users. Unfortunately, they also become fertile grounds for hackers to deploy malware and to spread virus. There is an urgent need to have a "security analytic & forensic system" which can facilitate analysts to examine, dissect, associate and correlate large number of mobile applications. An effective analytic system needs to address the following questions: How to automatically collect and manage a high volume of mobile malware? How to analyze a zero-day suspicious application, and compare or associate it with existing malware families in the database? How to perform information retrieval so to reveal similar malicious logic with existing malware, and to quickly identify the new malicious code segment? In this paper, we present the design and implementation of DroidAnalytics, a signature based analytic system to automatically collect, manage, analyze and extract android malware. The system facilitates analysts to retrieve, associate and reveal malicious logics at the "opcode level". We demonstrate the efficacy of DroidAnalytics using 150,368 Android applications, and successfully determine 2,494 Android malware from 102 different families, with 342 of them being zero-day malware samples from six different families. To the best of our knowledge, this is the first reported case in showing such a large Android malware analysis/detection. The evaluation shows the DroidAnalytics is a valuable tool and is effective in analyzing malware repackaging and mutations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2013.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2021DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecodecited by 9
- 2022Adversarial Patterns: Building Robust Android Malware Classifierscited by 0
- 2024On the Generalizability of Machine Learning-based Ransomware Detection in Block Storagecited by 0
- 2024On the Generalizability of Machine Learning-based Ransomware Detection in Block Storage30 Dec 2024
- 2022Adversarial Patterns: Building Robust Android Malware Classifiers4 Mar 2022
- 2021DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode5 Sep 2021