Mobile devices such as smartphones, netbooks and laptops are increasingly beginning to store vast amounts of personal information, such as email, friend lists, current location, and passwords to online banking websites. They are therefore swiftly becoming prized bounties for malicious entities. This project is investigating new techniques to detect malicious software on mobile devices.
This project is investigating three key thrusts (1) energy-aware malware detection, (2) malware detector protection using a new hardware architecture called Limited Local Memory, and (3) collaborative mobile malware detection in a social network environment. The first thrust develops a framework to quantitatively investigate how the energy-constrained nature of mobile devices impacts their ability to run malware detection tools. It is then using the framework to explore energy-aware malware detection. The second thrust explores how emerging multicore technology on smartphones can be usefully leveraged for security if each core is equipped with a small amount of private memory. The third trend explores how the convergence of smartphones and social networking can enable better malware detection.
The primary contribution of the project is in acknowledging the need for novel approaches for mobile malware detection. As we increasingly rely on our smartphones to support our daily activities, their security becomes paramount. The outcomes of the project are being disseminated to increase public awareness of mobile malware and anti-malware. In addition, course material related to the project will be incorporated into the Master's degree curriculum at Rutgers.
