Research on Collaborative Defense Technology of Network Security Based on Swarm Intelligence

doi:10.3969/j.issn.1671-1122.2020.09.011

Abstract

Abstract:

In this paper, a cooperative defense framework of network security based on swarm intelligence is designed, which is composed of data access and sharing, active prevention, joint perception and cooperative response, and an integrated learning algorithm with dynamic adjustment cooperative strategy is studied. it is applied to the attack event detection model to improve its detection accuracy. The cooperative response method of multiple security components based on bee colony algorithm is designed, and the experimental analysis is carried out, which provides the basic support for the research and realization of global awareness and cooperative defense among multiple security entities.

Key words: swarm intelligence, integrated learning, collaborative defense, bee colony algorithm

CLC Number:

TP309

ZENG Yingming, WANG Bin, GUO Min. Research on Collaborative Defense Technology of Network Security Based on Swarm Intelligence[J]. Netinfo Security, 2020, 20(9): 52-56.

Figures/Tables 3

References 9

[1]	ZHANG Sicong, XIE Xiaoyao, XU Yang. Intrusion Detection Method Based on a Deep Convolutional Neural Network[J]. Journal of Tsinghua University(Science and Technology), 2019,59(1):46-54.
	张思聪, 谢晓尧, 徐洋. 基于dCNN的入侵检测方法[J]. 清华大学学报(自然科学版), 2019,59(1):46-54.
[2]	SHONE N, NGOC T N, PHAIV D, et al. A Deep Learning Approach to Network Instrusion Detection[J]. IEEE Transations on Emerging Topics in Computational Intelligence, 2018,2(1):41-50.
[3]	WOLD S, ESBENSSEN K, GELADL P. Principal Component Analysis[J]. Chemometrics & Intelligent Laboratory System, 1987,2(1):37-52.
[4]	YAN Qiao, YU F R, GONG Qingxiang, et al. Software-defined Networking(SDN) and Distributed Denial of Service(DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges[J]. IEEE Communications Surveys \$ Tutorials, 2015,18(1):602-622.
[5]	SAHAY R, BLANC G, ZHANG Zonghua, et al. Towards Autonomic DDoS Mitigation Using Software Defined Networking[EB/OL]. https://hal.archives-ouvertes.fr/hal-01257899, 2019-9-10.
[6]	BRAGA R, MOTA E, PASSITO A. Lightweight DDoS Flooding Attack Detection Using NOX/OpenFlow[C]// IEEE. IEEE Local Computer Network Conference, October 10-14, 2010, Denver, CO, USA. New Jersey: IEEE, 2010: 408-415.
[7]	SYLVESTER E V A, BENTZEN P, BRADBURYIR, et al. Applications of Random Forest Feature Selection for Fine-Scale Genetic Population Assignment[J]. Evolutionary Applications, 2018,11(2):153-165. doi: 10.1111/eva.12524 URL pmid: 29387152
[8]	HART P E. The Condensed Nearest Neighbor Rule[J]. IEEE Transactions on Information Processing Systems, 2018,31(3):6638-6648.
[9]	JORDAN M I, BISHOP C. Neural Networks[J]. ACM Computing Surveys, 1996,28(1):73-75.

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