Its abstract is below. If it gets accepted, we will acknowledge SPARTA.
Abstract. Automotive industries are maximizing cooperative interac- tions between vehicular perception devices and infrastructure compo- nents to make intelligent decisions in its application (i.e traffic man- agement, navigation, or autonomous driving services). This cooperative behaviour also extends to security. With more connected and coopera- tive components of vehicular intelligent transportation systems (ITS), the possibility of malicious attacks against ITS ecosystem become highly visible, producing risks with high security and safety impact. The se- curity risks in one architecture layer impacts other layers of ITS, thus, cooperation for these systems to be securely operated is essential. This paper presents results from a comprehensive literature analysis on the state-of-the-art of security risk management in vehicular ITS, evaluating its assets, threats/risks and countermeasures. We examine these security elements along the dimensions of the perception, network and applica- tion architecture layers of ITS. The study reveals gaps in ITS security risk management research within these architecture layers and provides a proposition for future research in gap areas.
On 5 Jul 2019, at 10:33, Vivek Nigam <nigam@fortiss.org> wrote:
Dear all,--
We just got the information that our paper has been accepted to FMCAD.It is about a tool for generating automatically schedules for Time Sensitive Networks (TSN). More information below.
We will acknowledge SPARTA for the final version.
TSNsched: Automated Schedule Generation for Time Sensitive Networking
Time Sensitive Networking (TSN) is a set of standards enabling high performance deterministic communication using time scheduling. Due to the size of industrial networks, configuring TSN networks is challenging to be done manually. We present TSNsched, a tool for automatic generation of schedules for TSN. TSNsched takes as input the logical topology of a net- work, expressed as flows, and outputs schedules for TSN switches by using an SMT-solver. The generated schedule guarantees the desired network performance (specified in terms of latency and jitter), if such schedules exist. TSNsched supports unicast and multicast flows, such as, in Publish Subscribe networks; can combine existing variants of TSN scheduling problems, and reason about the best-effort and priority TSN traffic. TSNsched can be run as a standalone tool and also allows rapid prototyping with the available JAVA API. We evaluate TSNsched on a number of realistic-size network topologies. TSNsched can generate high performance schedules, with average latency less than 1000μs, and average jitter less than 20μs, for TSN networks, with up to 73 subscribers and up to 10 multicast flows.
Best,
Vivek Nigam
--
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