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MDoctor: A Mobile Malware Prognosis Application

Lagerspetz, E. and Truong, Hien Thi Thu and Tarkoma, S. and Asokan, N. (2014):
MDoctor: A Mobile Malware Prognosis Application.
In: Distributed Computing Systems Workshops (ICDCSW), 2014 IEEE 34th International Conference on, DOI: 10.1109/ICDCSW.2014.36,
[Conference or Workshop Item]

Abstract

Mobile malware is on the rise as the global number of smartphone users grows exponentially. Traditional malware detection and scanning tools only detect malware when devices are actually infected. In previous work, we saw that the presence of applications that occur often with known malware can indicate not only infection status but also potential risk of infection. In this paper, we present Doctor - a malware prognosis application based on crowd sourced data. Doctor includes a server component and an easy-to-use Android client application. Doctor visualizes the health of the device as a pie chart, slices representing applications. Each slice is split into four sections, corresponding to different lightweight indicators of infection. Sections of each slice are colored from green to red. The greater the amount of red, the greater the risk of infection. This front-end application provides users a new function for malware prognosis which is currently missing in existing mobile anti-malware tools.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Lagerspetz, E. and Truong, Hien Thi Thu and Tarkoma, S. and Asokan, N.
Title: MDoctor: A Mobile Malware Prognosis Application
Language: German
Abstract:

Mobile malware is on the rise as the global number of smartphone users grows exponentially. Traditional malware detection and scanning tools only detect malware when devices are actually infected. In previous work, we saw that the presence of applications that occur often with known malware can indicate not only infection status but also potential risk of infection. In this paper, we present Doctor - a malware prognosis application based on crowd sourced data. Doctor includes a server component and an easy-to-use Android client application. Doctor visualizes the health of the device as a pie chart, slices representing applications. Each slice is split into four sections, corresponding to different lightweight indicators of infection. Sections of each slice are colored from green to red. The greater the amount of red, the greater the risk of infection. This front-end application provides users a new function for malware prognosis which is currently missing in existing mobile anti-malware tools.

Title of Book: Distributed Computing Systems Workshops (ICDCSW), 2014 IEEE 34th International Conference on
Uncontrolled Keywords: ICRI-SC
Divisions: Profile Areas > Cybersecurity (CYSEC)
Profile Areas
Date Deposited: 31 Dec 2016 00:01
DOI: 10.1109/ICDCSW.2014.36
Identification Number: TUD-CS-2014-1034
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