Mobile Malware Detection through Analysis of Deviations in Application Network Behavior
Recently an exciting research on the topic of malware detection based on mobile networking activities analysis has been accepted to the lucrative Computers & Security journal. The paper was written as part of a project sponsored by Telekom Innovation Labs which dealt with different Android security solutions.
In simple words (though you'll need to get the full version to see all the exciting revelations and achievements) we built a technology which is able to detect malware activity based only on analyzing the network traffic coming out from a mobile handset (Android).
The full research can be accessed here
Here's the abstract:
In this paper we present a new behavior-based anomaly detection system for detecting meaningful deviations in a mobile application's network behavior. The main goal of the proposed system is to protect mobile device users and cellular infrastructure companies from malicious applications by: (1) identification of malicious attacks or masquerading applications installed on a mobile device, and (2) identification of republished popular applications injected with a malicious code (i.e., repackaging). More specifically, we attempt to detect a new type of mobile malware with self-updating capabilities that were recently found on the official Google Android marketplace. Malware of this type cannot be detected using the standard signatures approach or by applying regular static or dynamic analysis methods. The detection is performed based on the application's network traffic patterns only. For each application, a model representing its specific traffic pattern is learned locally (i.e., on the device). Semi-supervised machine-learning methods are used for learning the normal behavioral patterns and for detecting deviations from the application's expected behavior. These methods were implemented and evaluated on Android devices. The evaluation experiments demonstrate that: (1) various applications have specific network traffic patterns and certain application categories can be distinguished by their network patterns; (2) different levels of deviation from normal behavior can be detected accurately; (3) in the case of self-updating malware, original (benign) and infected versions of an application have different and distinguishable network traffic patterns that in most cases, can be detected within a few minutes after the malware is executed while presenting very low false alarms rate; and (4) local learning is feasible and has a low performance overhead on mobile devices.
The paper authors are:
- Dr. Asaf Shabtai
- Dr. Lena Tenenboim-Chekina
- Dudu Mimran
- Prof. Lior Rokach
- Prof. Bracha Shapira
- Prof. Yuval Elovici