Name
Abstract | ||
|---|---|---|
Mobile malware has gained significant ground since the dawning of smartphones and handheld devices. TrendLabs estimated that there were 718,000 malicious and high risk Android apps in the second quarter of 2013. Mobile malware malicious infections arise through various techniques such as installing repackaged legitimate apps with malware, updating current apps that piggy back malicious variants, or even a drive-by download. The infections themselves will perform at least one or multiple of the following techniques, privilege escalation, remote control, financial charge, and information collection, etc. This paper summarizes mobile malware threats and attacks, cybercriminal motivations behind malware, existing prevention methods and their limitations, and challenges encountered when preventing malware on mobile devices. The paper further proposes a cloud-based framework for mobile malware detection. The proposed framework requires a collaboration among mobile subscribers, app stores, and IT security professionals. The cloud-based malware detection is a promising approach towards mobile security. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CTS.2014.6867562 | Collaboration Technologies and Systems |
Keywords | Field | DocType |
cloud computing,invasive software,mobile computing,Android applications,TrendLabs,cloud-based detection,cybercriminal motivations,financial charge technique,handheld devices,information collection technique,mobile malware detection,mobile malware malicious infections,mobile malware security,privilege escalation technique,remote control technique,smart phones,Android,cloud,detection,malware,mobile,security | Mobile malware,Mobile computing,Cryptovirology,Mobile search,Internet privacy,Android (operating system),Application security,Computer science,Computer security,Mobile device,Malware | Conference |
Citations | PageRank | References |
5 | 0.48 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas Penning | 1 | 5 | 0.48 |
| Michael Hoffman | 2 | 5 | 0.48 |
| Jason Nikolai | 3 | 24 | 2.58 |
| Yong Wang | 4 | 93 | 12.91 |