On the Use of Mobile GPU for Accelerating Malware Detection Using Trace Analysis

Abstract

Malware detection on mobile phones involves analysing and matching large amount of data streams against a set of known malware signatures. Unfortunately, as the number of threats grows continuously, the number of malware signatures grows proportionally. This is time consuming and leads to expensive computation costs, especially for mobile devices where memory, power and computation capabilities are limited. As the security threat level is getting worse, parallel computation capabilities for mobile phones is getting better with the evolution of mobile graphical processing units (GPUs). In this paper, we discuss how we can benefit from the evolving parallel processing capabilities of mobile devices in order to accelerate malware detection on Android mobile phones. We have designed and implemented a parallel host-based anti-malware for mobile devices that exploits the computation capabilities of mobile GPUs. A series of computation and memory optimization techniques are proposed to increase the detection throughput. The results suggest that mobile graphic cards can be used effectively to accelerate malware detection for mobile phones.