Pavlidis, Michalis

Polatidis, Nikolaos

Seraj, Saeed

English

Sharma A.

Lee W. Y.

Arora A.

Crossref

A Novel Dataset for Fake Android Anti-Malware Detection

Today in the world people are able to get all types of Android applications (apps) from the app store or various sources over the Internet. A large number of apps is being produced daily, some of which are infected with malware. Thus, the use of anti-malware identification tools is essential. At the same time, a number of attackers who exploit a number of anti-malwares have been doing obtaining information from mobile phones in various ways, such as decompiling or infecting anti-malware. Therefore, in this paper, we developed a classification dataset from collected anti-malware data looking for fraudulent anti-malware products. Additionally, we applied various machine learning algorithms and we propose a combination of algorithms which provides high accuracy over various evaluation tests, showing that our approach is both practical and effective.</p

University of Brighton Research Portal

A novel dataset for fake android anti-malware detection

https://cris.brighton.ac.uk/ws/files/13231989/wims.pdf

A novel dataset for fake android anti-malware detection

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University of Brighton Research Portal