Development and Detection of Mobile Malware
Abstract
This project provides us with a thorough understanding of the potential development of mobile malware, and it helps develop effective countermeasures to detect mobile malware. It greatly improves the security situation on mobile phone networks (and the Internet) and benefit the broad community of the smartphone users, including U.S. government agencies and military personals. The proposed research compares the feasibility of three well known machine learning algorithms on the detection of malware on the Android platform. Once accuracy is at an acceptable level, these algorithms performance are further enhanced to decrease analysis time, which can lead to faster detection rates. The framework makes use of powerful GPUs (Graphics Processing Unit) in order to reduce the time spent on computation for malware detection. Utilizing MATLABs parallel computing kit, we can execute analysis at a much higher speed due to the increased cores in the GPU. A reduced computation time allows for quick updates to the user about zero day malware, resulting in a decreased impact. With the increase in mobile devices unending, quick detection becomes necessary to combat mobile malware, and with Android alone reaching its 50 billionth app downloads, is no small task.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 20, 2016
- Accession Number
- AD1063521
Entities
People
- Hongmei Chi
Organizations
- Florida A&M University