Robust Artificial Intelligence Systems to Improve Security of Smartphone Users

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The wide-ranging capabilities of modern*smartphones have redefined how we work and entertain ourselves. Unfortunately,*this is accompanied with an increase in the number and sophistication of*threats to personal and corporate data. Several AI-based solutions have been*put forth that provide defences against these threats. For instance,*unauthorized usage of smartphones by non-owners is a threat that is addressed*by AI-based continuous authentication solutions. Continuous authentication*products rely on the device usage behaviour of smartphone owners (e.g., typing*or swiping behaviour). Similarly, AI-based fraud detection*methods address the threat of fraudulent transactions by malware on*smartphones. ******While these solutions improve security under*some circumstances, they do not consider attackers that attempt to actively defeat*them. As a result, these solutions may not provide the desired level of*security. The long-term goal of the proposed research is to evaluate the security properties of AI-based security solutions against human adversaries who will assume different attack capabilities, propose improvements to the existing systems so*that they are resilient against human adversaries, and the identification of the most effective way to communicate properties of these methods to users so that they can choose the configuration that works best for*them while minimizing incidents due to miscomprehension and misconfiguration. The near-term goal of this research is to bolster the security of two AI-based systems that are currently being used to improve the security of mobile users, continuous authentication systems and device fingerprinting, against imitation-based attacks ("mimicry attacks") from human adversaries.******Exciting times are*ahead: enterprises are increasingly adopting AI-based continuous authentication*solutions like Samsung BioCatch and IBM Trusteer. Financial institutions are widely*using AI-based fraud detection methods to flag potentially fraudulent*transactions. While these efforts hold great promise, it is critical to carefully evaluate the*security offered by these systems against resourceful adversaries. Our research will ensure that*security practitioners and product designers are not only aware of the present*limitations of the AI-based security systems but also aware of the measures they*can take to make these solutions more robust against active adversaries. Finally,*by providing controls to the end users, we will address the often neglected*human side of these systems.