While being both extremely powerful and popular, TLS is a protocol that is hard to securely deploy. On the one hand, system administrators are required to grasp several security concepts to fully understand the impact of each option and avoid misconfigurations. On the other hand, app developers should use cryptographic libraries in a secure way avoiding dangerous default settings or other subtleties (e.g., padding or modes of operations). To help secure TLS, we propose a modular framework, extensible with new features and capable of streamlining the mitigation process of known and newly discovered TLS attacks even for non-expert users.

To grasp the security implications of the various TLS configuration options, system administrators and app developers must be familiar with a wide range of concepts, including cryptography. To assist users in this task, we propose TLSAssistant- a modular and extensible framework designed to streamline the discovery and mitigation of potential vulnerabilities in TLS deployments. This demo will focus on two of the four available analysis types.

This study presents a triggering algorithm for a collaborative, motorcycle-to-car collision avoidance system that slows down the car without input of the driver when the collision becomes imminent. The algorithm is based on the concept of inevitable state collisions. Example applications of the proposed algorithm were obtained via 2D computer simulations representing a data set of real crashes occurred in Italy, Sweden and Australia. Results indicated that the proposed method can apply to typical crash scenarios.