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Given that security threats and privacy breaches are com- monplace today, itis an important problem for one to know whether their device(s) are in a "goodstate of security", or is there a set of high- risk vulnerabilities that needto be addressed. In this paper, we address this simple yet challenging problem.Instead of gaining white-box access to the device, which offers privacy andother system issues, we rely on network logs and events collected offine aswell as in realtime. Our approach is to apply analytics and machine learningfor network security analysis as well as analysis of the security of theoverall device - apps, the OS and the data on the device. We propose techniquesbased on analytics in order to determine sensitivity of the device,vulnerability rank of apps and of the device, degree of compromise of apps andof the device, as well as how to define the state of security of the devicebased on these metrics. Such metrics can be used further in machine learningmodels in order to predict the users of the device of high risk states, and howto avoid such risks.

Security Analytics of Network Flow Data of IoT and Mobile Devices (Work-in-progress)