Enabling GPU-assisted Antivirus Protection on Android Devices through Edge Offloading

Dimitris Deyannis (Lecturer), Rafail Tsirbas (Lecturer), Giorgos Vasiliadis (Lecturer), Raffaele Montella (Lecturer), Kosta, S. (Lecturer), Sotiris Ioannidis (Lecturer)

Activity: Talks and presentationsConference presentations


Antivirus software are the most popular tools for detecting and stopping malicious or unwanted files.
However, the performance requirements of traditional host-based antivirus make their wide adoption to mobile, embedded, and hand-held devices questionable.
Their computational- and memory-intensive characteristics,
which are needed to cope with the evolved and sophisticated malware, makes their deployment to mobile processors a hard task. Moreover, their increasing complexity may result in vulnerabilities that can be exploited by malware.
In this paper, we first describe a GPU-based antivirus algorithm for Android devices. Then, due to the limited number of GPU-enabled Android devices, we present different architecture designs that exploit code offloading for running the antivirus on more powerful machines.
This approach enables lower execution and memory overheads, better performance, and improved deployability and management.
We evaluate the performance, scalability, and efficacy of the system in several different scenarios and setups. We show that the time to detect a malware is \textbf{8.4 times}
lower than the typical local execution approach.
Period10 Jun 2018
Event title1st International Workshop on Edge Systems, Analytics and Networking (in conjuction with MobiSys '18)
Event typeWorkshop
Conference number1
LocationMunich, Germany
Degree of RecognitionInternational