The algorithm design of anti-attack performance testing software in large network
- DOI
- 10.2991/amcce-15.2015.208How to use a DOI?
- Keywords
- large network; network environment; anti-attack performance
- Abstract
This paper studied anti-attack performance testing methods in large network. Anti-attack performance testing process in large networks is different from traditional testing process, which mainly features harbinger attacks, lacking precise characteristic information of determining the attack act. Point to point structure limit characteristics connections, traditional detection method focuses on fixed feature information directly linked to take anti-attack performance testing, once lost contact feature will cause the detection inaccuracy. In order to avoid these shortcomings, this paper proposed an anti-attack performance testing method in large networks based on fuzzy C-means clustering algorithm. Collected relevant data to extract and analysis sample characteristics, the use of fuzzy C-means clustering method for classification of data for further calculations, to gain abnormal behavior pattern data to complete anti-attack performance testing in large network. Experimental results showed that using the proposed algorithm for anti-attack performance testing in large network could greatly improve the accuracy of detection, so as to effectively maintain a large network security, and to provide users with good network environment.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ling Zhang PY - 2015/04 DA - 2015/04 TI - The algorithm design of anti-attack performance testing software in large network BT - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering PB - Atlantis Press SP - 623 EP - 627 SN - 1951-6851 UR - https://doi.org/10.2991/amcce-15.2015.208 DO - 10.2991/amcce-15.2015.208 ID - Zhang2015/04 ER -