Protecting Analog Sensor Security (alt: Sending Mixed Signals on IoT Cybersecurity)

Why are undergraduates taught to hold the digital abstraction as sacrosanct and unquestionable?  Why do microprocessors blindly trust input from sensors, and what can be done to establish trust in unusual input channels in cyberphysical systems? Risks of analog sensor cybersecurity pose challenges to autonomous vehicles, medical devices, and the Internet of Things.

Privacy Preserving User Profiling Using Net2Vec

We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network. Net2Vec is able to capture data from the network at more than 60Gbps, transform it into meaningful tuples and apply predictions over the tuplesin real time. This platform can be used for different purposes ranging from traffic classification to network performance analysis. Finally, we showcasethe use of Net2Vec by implementing and testing a solution able to profile network users at line rate using traces coming from a real network. We show that the use of deep learning for this case outperforms the baseline method both in terms of accuracy and performance.

Interval Graph Completion and Polynomial-Time Preprocessing


This talk will start by arguing that the complexity class FPT can be used to capture the notion of polynomial-time preprocessing to reduce input size. This is followed by an FPT algorithm with runtime $O(n^{2k}n^{3}m)$ for the following NP-complete problem [GT35 in Garey&Johnson]: Given an arbitrary graph G on n vertices and m edges, can we obtain an interval graph by adding at most k edges to G? The given algorithm answers a question first posed by Kaplan, Shamir and Tarjan in 1994.


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