{"id":548172,"date":"2018-11-06T09:49:53","date_gmt":"2018-11-06T17:49:53","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/?post_type=msr-research-item&p=548172"},"modified":"2018-11-06T09:49:53","modified_gmt":"2018-11-06T17:49:53","slug":"plagiarizing-smartphone-applications-attack-strategies-and-defense-techniques","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/plagiarizing-smartphone-applications-attack-strategies-and-defense-techniques\/","title":{"rendered":"Plagiarizing Smartphone Applications: Attack Strategies and Defense Techniques"},"content":{"rendered":"

In this paper, we show how an attacker can launch malware onto a large number of smartphone users by plagiarizing Android applications and by using elements of social engineering to increase infection rate. Our analysis of a dataset of 158,000 smartphone applications meta-information<\/em> indicates that 29.4% of the applications are more likely to be plagiarized. We propose three detection schemes that rely on syntactic fingerprinting to detect plagiarized applications under different levels of obfuscation used by the attacker. Our analysis of 7,600 smartphone application binaries<\/em> shows that our schemes detect all instances of plagiarism from a set of real-world malware incidents with 0.5% false positives and scale to millions of applications using only commodity servers.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this paper, we show how an attacker can launch malware onto a large number of smartphone users by plagiarizing Android applications and by using elements of social engineering to increase infection rate. Our analysis of a dataset of 158,000 smartphone applications meta-information indicates that 29.4% of the applications are more likely to be plagiarized. 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