A Novel Dataset for Fake Android Anti-Malware Detection
Year: 2020
Type: conference-paper
Abstract: 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.
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3047201250