Masoomeh Beitsayahi - Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
Said Seraj - Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
Parisa Daneshjoo - Department of Computer Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran

Today in the world people are able to get all types of Android applications(apps) from the markets in the cyberspace. In the world, a large number of apps is being produceddaily, some of which are infected with malware. Hence, we need anti-malware to identify malware types. Meanwhile, a number of exploiters who exploit a number of these antimalwares have been doing profitable practices and obtaining information from mobile phones in various ways, such as decompiling or infecting anti-malware. In the study, we collected 246 anti-malware protocols, among which we were looking for fraudulent anti-malware products, and finally, using the algorithms of machine learning, we identified them and using the 3 algorithms we found the results to be highly accurate. To identify these malwares, we used features such as permissions and file size and identify them by the VirusTotal website and obtaining labels from Dr. Web s anti-malware site.

Malware_Anti-Malware_Android_Machine Learning