Abstract-Network traffic is a rich source of information for security monitoring. However the increasing volume of data to treat raises issues, rendering holistic analysis of network traffic difficult. In this paper we propose a solution to cope with the tremendous amount of data to analyse for security monitoring perspectives. We introduce an architecture dedicated to security monitoring of local enterprise networks. The application domain of such a system is mainly network intrusion detection and prevention, but can be used as well for forensic analysis. This architecture integrates two systems, one dedicated to scalable distributed data storage and management and the other dedicated to data exploitation. DNS data, NetFlow records, HTTP traffic and honeypot data are mined and correlated in a distributed system that leverages state of the art big data solution. Data correlation schemes are proposed and their performance are evaluated against several well-known big data framework including Hadoop and Spark.

In Smart TV Live Multicast systems, packet loss is one critical factor that causes OoS(Quality of Service) decreases, OPRSFEC (Optimized Reed-Solomon Forward Error Correction) provides an end-to-end method of packet loss recovery in the application layer which can recover lost packets completely (when the loss rate is less than 5%) with few computational resources and very little impact on users' real-time experience. The OPRSFEC middleware has become a production-level technology which forms part of the IPTV2.0 standard in Shanghai Telecom and has been running on Mstar boards in Moca model television sets of brand Haier.

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