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Internet of Things (IoT) devices, web browsers, phones, and even cars may be fingerprinted for tracking, and their connections routed through or to malicious entities. When IoT devices interact with a remote service, the integrity or authentication of that service is not guaranteed. IoT and other edge devices could be subject to man-in-the-middle (MiTM) attacks, with IoT devices attempting to connect to remote services. It is also straight-forward to use phishing or pharming to convince a user to accept a connection to a potentially malicious unfamiliar device. These risks could be mitigated by leveraging information on the edge of the network about the path to and destination of a connection. In this work we sample packets, then use packet analysis and local history to identify risky or suspicious connections. In contrast to other machine learning and big data approaches, the use of local data enables risk detection without loss of privacy.
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