Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Android community, a commonly held belief is that rooted devices are more at-risk than their standard counterparts. Many applications include "protections" that disable certain functionality in the presence of a "rooted" device. ,  carry out a detailed quantitative analysis on 6.14 million Android devices comparing rooted and non-rooted devices across a broad range of characteristics. Specifically our study focuses on measuring and analyzing various behavioral differences beten rooted and non-rooted devices, and how such differences impact the security of rooted devices, e.g. making them more susceptible to malware or at higher risk.  further leverage our insights and implement a machine learning system to determine whether a device is rooted based on a comprehensive set of features with an accuracy of approximately 90%.
['Yun Shen', 'Nathan S. Evans', 'Azzedine Benameur']
I am writing a research paper on a idea Insights into rooted and non-rooted Android mobile devices with behavior analytics
[{"from": "human", "value": "I am writing a research paper on a idea Insights into rooted and non-rooted Android mobile devices with behavior analytics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Android community, a commonly held belief is that rooted devices are more at-risk than their standard counterparts. Many applications include \"protections\" that disable certain functionality in the presence of a \"rooted\" device. ,  carry out a detailed quantitative analysis on 6.14 million Android devices comparing rooted and non-rooted devices across a broad range of characteristics. Specifically our study focuses on measuring and analyzing various behavioral differences beten rooted and non-rooted devices, and how such differences impact the security of rooted devices, e.g. making them more susceptible to malware or at higher risk.  further leverage our insights and implement a machine learning system to determine whether a device is rooted based on a comprehensive set of features with an accuracy of approximately 90%."}]