Abstract
This paper puts forward a network security computing platform which based on the open source big data technology. This network anomaly traffic detection system includes a comprehensive and effective traffic anomaly detection algorithm. The algorithm combines the exponentially weighted moving average algorithm (EWMA) and the anomaly flow interval mapping algorithm two methods. The test experiments contain the physical cluster building parameters, the performance of the system and the DDoS attack verification. It is verified by the Tianjin Education metropolitan area network traffic and the NSFOCUS against denial attack event log service system (NSFOCUS ANTI-DDoS system). In Particular, the network anomaly traffic detection system can accurately identify the occurrence period of anomaly traffic such as the flow direction and the event priority. What’ more, it has strong fault tolerance which can achieve error recovery in a short time. All the test experiment results can verify that the system can effectively detect anomaly traffic and real-time monitor the dynamic network.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
