ZHOU Aiping, LIU Lijun, ZHU Huisheng, et al., “Parallel Sketch Based Super Node Detection with Traceability,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1133-1140, 2018, doi: 10.1049/cje.2018.08.009
Citation: ZHOU Aiping, LIU Lijun, ZHU Huisheng, et al., “Parallel Sketch Based Super Node Detection with Traceability,” Chinese Journal of Electronics, vol. 27, no. 6, pp. 1133-1140, 2018, doi: 10.1049/cje.2018.08.009

Parallel Sketch Based Super Node Detection with Traceability

doi: 10.1049/cje.2018.08.009
Funds:  This work is supported by Open Project Foundation of Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China (No.K93-9-2017-01), Fund for "Integration of Cloud Computing and Big Data, Innovation of Science and Education", Ministry of Education, China (No.2017B06109), Natural Science Foundation of Jiangsu, China (No.BRA2015212, No.BK20141307), Scientific Research Foundation for Advanced Talents of Taizhou University, China (No.QD2016027), and Technology Support Project of Taizhou, China (No.TS201633).
  • Received Date: 2016-09-30
  • Rev Recd Date: 2017-10-23
  • Publish Date: 2018-11-10
  • Traffic measurement and monitoring is crucial for network applications, such as network security, network management and so on. One central problem is to detect super nodes, which have significant change of connection degree between consecutive measurement periods. Due to weakness in massive network traffic processing for the centralized algorithm and low detection accuracy, space efficiency for super node detection algorithm based on flow sampling, we propose Parallel sketch based super node detection with traceability (PSD). It constructs parallel sketch and estimates connection degree of nodes by probabilistic counting approach, so that super nodes are identified using connection degree change between consecutive measurement periods. Moreover, IP addresses of super nodes are reconstructed by simple computing to trace attacker or victims. The experimental results illustrate that the proposed method outperforms the Compact spread estimator (CSE) and Data streaming and sampling (DSS) in terms of detection accuracy and storage utilization.
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