Abstract
The problem of malwares affecting Smartphones has been widely recognized by the researchers across the world. Majority of these malwares target Android OS. Studies have found that most of the Android malwares hide inside repackaged apps to get inside user devices. Repackaged apps are usually infected versions of popular apps. Adversaries download a popular Android app, and obtain the code using reverse engineering and then add their code (often malicious) to it and repackage and release the app. A number of techniques proposed in research and a number of commercial anti-virus products focus on detecting malwares. Repacked mobile apps constitute to about 78% of the all malwares of android and it greatly affects the technical ecosystem of Android. Although there are many methods exist for the repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern Feature extraction based Hashing is proposed for the identification of repacked android applications through the visual similarity which is a notable feature of repacked applications. The results from the experiment proves that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it is proved that the proposed CKPS:LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95,124 applications and also the computational time was 0.91 s within which a user could get decision of whether the app is repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.