Security and Features Level Assessment for Mobile Application

Nowadays, with the fast development of mobile computing and thus the technology market, the mobile app becomes is that the most well-liked. Within the mobile App market rankings ask fraud as deceptive or duplicate actions intended to push apps into the popularity list. For seniority on leader-boards they cheat the app by downloading the app through various devices and provide fake ratings and reviews using human teams and bot farms. A higher rank on leader-boards usually results in download and million-dollar revenue so we will undergo some technology for each application to look at fraud within the app. During this paper on our project "security and features level assessment for mobile applications," we provide a full view of ranking fraud and develop a ranking fraud detection system for mobile Apps. We firstly determine the ranking fraud by mining the active sessions, namely leading sessions of mobile Apps. To finding a ranking by this sort of leading sessions are often beneficial. During this investigation the precision of deceptive actions in ranking, the performance of the proposed system and show the scalability of the detection algorithm.