Fake Applications Discovered On The Google Play Store

Thousands of popular fake applications discovered on the Google Play Store

      The use of malware or dangerous applications is unavoidable, given the scale of Google Play Store operations. 

     Google has put in place a number of standards to prevent fake applications from accessing the Play Store, but it is virtually impossible to get rid of every one of them.

      A group of researchers from the University of Sydney and Data61 at CSIRO has discovered more than 2,000 fake applications, mimicking popular applications and games. Together, the two institutions reviewed more than a million applications over two years and the results were staggering.

          Temple Run and more

     The 2040 applications identified as malware or forgeries include copies of popular games such as Temple Run, Free Flow, Hill Climb Racing and some formatting and photo editing applications. These applications have hidden malware or require unnecessary permissions.

     Almost all of these applications have over 100 million downloads, including 500 million in the case of Hill Climb Racing. While some of these copied applications are malware, others are harmless imitations that want to make quick money by using the brand name. However, the extra permissions required by the fake applications give them access to your data, which is a significant risk.

          Dr. Suranga Seneviratne of the USyd School of Computer Science said:

     Many fake applications look innocent and legitimate - smartphone users can easily fall victim to application identity theft and even a technology-savvy user can have trouble detecting them before installation. 

     In an open application ecosystem like Google Play, the barrier to entry is low, making it relatively easy for fake applications to infiltrate the market, exposing users to the risk of hacking.

     While the success of Google Play is marked by its flexibility and customizable features that allow almost anyone to create an application, many problematic applications have escaped the crack and bypassed automated filtering processes.

          Machine learning and neural networks

     Researchers used neural networks and machine learning to analyze 1 million applications. The algorithm was designed to search for textual and visual similarities with the 10,000 famous applications in the Google Play Store.

     The algorithm returned approximately 49,608 threats, of which 2,040 were high-risk applications, 7,246 were reported as malicious, 1,565 sensitive permissions were requested, and 1,077 third-party libraries were embedded for advertisements.