Robust deployment strategy for security data collection agent

被引：0

作者：

Chen L. ^{[1
,2
]}

Wang Z. ^{[2
,3
]}

Guo Y. ^{[2
]}

Hua J. ^{[1
,2
]}

Yao Y. ^{[1
]}

Li F. ^{[1
,2
,4
]}

机构：

[1] State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an

[2] Institute of Information Engineering, Chinese Academy of Sciences, Beijing

[3] School of Cyberspace, Hangzhou Dianzi University, Hangzhou

[4] School of Cyber Security, University of Chinese Academy of Sciences, Beijing

来源：

Tongxin Xuebao/Journal on Communications | 2019年 / 40卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Collection agent; Defender-attacker game theory; Deployment strategy; Robust; Security data;

D O I：

10.11959/j.issn.1000-436x.2019121

中图分类号：

学科分类号：

摘要：

With the frequent occurrence of "network black production" incidents, attackers strategically launch target attacks with the idea of "profit-seeking". Existing network monitoring systems lack accurate and effective monitoring strategies for "strategic attacks". Therefore, in an adversarial environment, how to optimize the deployment of collection agents for better monitoring results becomes an extremely important issue. Based on this, a robust deployment strategy of collection agents was proposed for the above mentioned problem. Firstly, the idea of attack-defense game was introduced to measure the collection agents, threat events and their relations, then the MADG model was built. Secondly, considering that the traditional accurate solution algorithm cannot solve the problem, the robust acquisition agent deployment algorithm called RCD algorithm was designed to approximate the problem by using the sub-module and non-growths of the objective function. Finally, the RCD algorithm was verified. The experimental results show that the above model and method is feasible, effective and expandable. © 2019, Editorial Board of Journal on Communications. All right reserved.

引用

页码：51 / 65

页数：14

共 26 条

[1] Ma L.B., Li X., Zhang L., On modeling and deploying an effective scan monitoring system, Journal of Software, 20, 4, pp. 845-857, (2009)
[2] Talele N., Teutsch J., Erbacher R., Et al., Monitor placement for large-scale systems, The 19th ACM Symposium on Access Control Models and Technologies (SACMT'14), pp. 29-40, (2014)
[3] Aqil A., Resource efficient frameworks for network and security problems, (2017)
[4] Breitbart Y., Chan C.Y., Garofalakis M., Et al., Efficiently monitoring bandwidth and latency in IP networks, INFOCOM, pp. 1-10, (2001)
[5] Hochbaum D.S., Approximation Algorithm for NP-Hard Problems, (1997)
[6] Suh K., Guo Y., Kurose J., Et al., Locating network monitors: complexity, heuristics and coverage, Infocom 2005, pp. 351-361, (2005)
[7] Chaudet C., Fleury E., Guerin Lassous I., Et al., Optimal positioning of active and passive monitoring devices, The CoNEXT, pp. 71-82, (2005)
[8] Cai Z.P., Liu F., Zhao W.T., Et al., Deploying models and optimization algorithms of network measurement, Journal of Software, 19, 2, pp. 419-431, (2008)
[9] Leskovec J., Krause A., Guestrin C., Et al., Cost-effective outbreak detection in networks, The 13th ACM SIGKDD International Conference on Knowledge Discovery and Datamining, pp. 420-429, (2007)
[10] Krause A., McMahan B., Guestrin C., Et al., Selecting observations against adversarial objectives, International Conference on Neural Information Processing Systems, pp. 777-784, (2007)

← 1 2 3 →