Lagerspetz, E. ; Truong, Hien Thi Thu ; Tarkoma, S. ; Asokan, N. (2014)
MDoctor: A Mobile Malware Prognosis Application.
doi: 10.1109/ICDCSW.2014.36
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Lagerspetz, E. ; Truong, Hien Thi Thu ; Tarkoma, S. ; Asokan, N. |
| Art des Eintrags: | Bibliographie |
| Titel: | MDoctor: A Mobile Malware Prognosis Application |
| Sprache: | Deutsch |
| Publikationsjahr: | Juni 2014 |
| Buchtitel: | Distributed Computing Systems Workshops (ICDCSW), 2014 IEEE 34th International Conference on |
| DOI: | 10.1109/ICDCSW.2014.36 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | ICRI-SC |
| ID-Nummer: | TUD-CS-2014-1034 |
| Fachbereich(e)/-gebiet(e): | Profilbereiche > Cybersicherheit (CYSEC) Profilbereiche |
| Hinterlegungsdatum: | 31 Dez 2016 00:01 |
| Letzte Änderung: | 16 Mai 2018 12:47 |
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