In the current paper, we present our work towards accelerating intrusion detection operations at the edge network using FPGAs.Cloud computing and network function virtualization have led to a new appealing paradigm for service delivery and management. Unfortunately, this paradigm fails to correctly support IoT applications and services that seek better communication platforms. Security as a Service can also be seen as a cloud-based model that needs to be accommodated to fulfill these services requirements. Again, one of the main issues to be addressed in this context is how to improve the performance of such systems or services in order to make them capable of coping with the huge amount of data while remaining reliable. A potential solution is the FPGA based edge computing, which is a powerful combination offering FPGA acceleration capabilities together with edge and fog benefits. Indeed, our work focusses on devising an Intrusion Prevention architecture called FORTISEC (40SEC), that is meant to operate in a completely softwarized as well as in an FPGA accelerated mode. Thereby, we present suitable algorithms, design methodologies and well defined components towards the implementation of accelerated intrusion prevention on the edge. It is worth to mention that although 40SEC is discussed here in the context of edge computing, it can serve as a security solution for any Small and Medium Enterprise looking for full protection of its network at a reasonable price. We also present a testbed being utilized for the implementation of 40SEC and its performance testing.
Great research efforts are made towards transferring additional data, such as location and sensor information, within the duration of an NG112 call. Especially in the field of emergency communication, the transmission of eHealth sensor data, which provide information about the vital parameters of a person in need of help, could improve the overall rescue operation. Since emergency calls are time-critical, it is necessary to analyse the impact of the attached sensor data on the emergency call system. In this paper, we present a TTCN-3 Test System that emulates smart devices by sending simulated sensor data via Bluetooth to a smartphone to trigger automated emergency calls. This emergency call is routed through an NG112 platform back to the Test System to measure the latency between sending the data and receiving the call. By incrementing the number of simulated sensors, the impact of the attached sensor data can be evaluated.
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