Biblio

With current IoT architectures, once a single device in a network is compromised, it can be used to disrupt the behavior of other devices on the same network. Even though system administrators can secure critical devices in the network using best practices and state-of-the-art technology, a single vulnerable device can undermine the security of the entire network. The goal of this work is to limit the ability of an attacker to exploit a vulnerable device on an IoT network and fabricate deceitful messages to co-opt other devices. The approach is to limit attackers by using device proxies that are used to retransmit and control network communications. We present an architecture that prevents deceitful messages generated by compromised devices from affecting the rest of the network. The design assumes a centralized and trustworthy machine that can observe the behavior of all devices on the network. The central machine collects application layer data, as opposed to low-level network traffic, from each IoT device. The collected data is used to train models that capture the normal behavior of each individual IoT device. The normal behavioral data is then used to monitor the IoT devices and detect anomalous behavior. This paper reports on our experiments using both a binary classifier and a density-based clustering algorithm to model benign IoT device behavior with a realistic test-bed, designed to capture normal behavior in an IoT-monitored environment. Results from the IoT testbed show that both the classifier and the clustering algorithms are promising and encourage the use of application-level data for detecting compromised IoT devices.

To increase security and to offer user experiences according to the requirements of a hyper-connected world, modern vehicles are integrating complex electronic systems, being transformed into systems of Cyber-Physical Systems (CPS). While a great diversity of heterogeneous hardware and software components must work together and control in real-time crucial functionalities, cybersecurity for the automotive sector is still in its infancy. This paper provides an analysis of the most common vulnerabilities and risks of connected vehicles, using a real example based on industrial and market-ready technologies. Several components have been implemented to inject and simulate multiple attacks, which enable security services and mitigation actions to be developed and validated.

When a group of individuals and organizations wish to compute a stable matching–-for example, when medical students are matched to medical residency programs–-they often outsource the computation to a trusted arbiter in order to preserve the privacy of participants' preferences. Secure multi-party computation offers the possibility of private matching processes that do not rely on any common trusted third party. However, stable matching algorithms have previously been considered infeasible for execution in a secure multi-party context on non-trivial inputs because they are computationally intensive and involve complex data-dependent memory access patterns. We adapt the classic Gale-Shapley algorithm for use in such a context, and show experimentally that our modifications yield a lower asymptotic complexity and more than an order of magnitude in practical cost improvement over previous techniques. Our main improvements stem from designing new oblivious data structures that exploit the properties of the matching algorithms. We apply a similar strategy to scale the Roth-Peranson instability chaining algorithm, currently in use by the National Resident Matching Program. The resulting protocol is efficient enough to be useful at the scale required for matching medical residents nationwide, taking just over 18 hours to complete an execution simulating the 2016 national resident match with more than 35,000 participants and 30,000 residency slots.