Biblio

Cyber breaches can result in disruption to business operations, reputation damage as well as directly affecting the financial stability of the targeted corporations, with potential impacts on future profits and stock values. Automatic network-stream monitoring becomes necessary for cyber situation awareness, and time-series anomaly detection plays an important role in network stream monitoring. This study surveyed recent research on time-series analysis methods in respect of parametric and non-parametric techniques, and popular machine learning platforms for data analysis on streaming data on both single server and cloud computing environments. We believe it provides a good reference for researchers in both academia and industry to select suitable (time series) data analysis techniques, and computing platforms, dependent on the data scale and real-time requirements.

A5-1 algorithm is a stream cipher used to encrypt voice data in GSM, which needs to be realized with high performance due to real-time requirements. Traditional implementation on FPGA or ASIC can't obtain a trade-off among performance, cost and flexibility. To this aim, this paper introduces CGRCA to implement A5-1, and in order to optimize the performance and resource consumption, this paper proposes a resource-based path seeking (RPS) algorithm to develop an advanced implementation. Experimental results show that final optimal throughput of A5-1 implemented on CGRCA is 162.87Mbps when the frequency is 162.87MHz, and the set-up time is merely 87 cycles, which is optimal among similar works.