MMP: A Dynamic Routing Protocol Design to Proactively Defend against Wireless Network Inference Attacks
Pages 1 - 11
Abstract
Network inference refers to the process of extracting sensitive information from a network without directly accessing it. This poses a significant threat to network security since it allows attackers to gain insight into sensitive information such as flow information through inference. Possessing flow information about a wireless network can empower attackers to launch more sophisticated and targeted attacks. Network inference relies on consistent traffic patterns or behavior to establish the relationship between the measured link metrics and flow information. Therefore, dynamic routing can help enhance resilience against network inference by proactive introducing variability into network traffic patterns, which can incur a high probability of mismatch between the observed patterns and the actual ones. In this paper, we observe that the inference error is positively related to the mismatch. Therefore, we propose a dynamic routing protocol, called Max-Mismatch-Probability (MMP), which seeks to maximize mismatch probability and increase the inference error. In this paper, we provide the theoretical analysis of our proposed protocol and show that the inference error of MMP is Θ(√N), which is verified in our experimental results.
References
[1]
Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. IEEE Std 802.11, 2013.
[2]
Novella Bartolini, Ting He, Viviana Arrigoni, Annalisa Massini, Federico Trombetti, and Hana Khamfroush. On fundamental bounds on failure identifiability by boolean network tomography. IEEE/ACM Transactions on Networking, 28(2):588-- 601, 2020.
[3]
Ilker Bekmezci, Ozgur Koray Sahingoz, and amil Temel. Flying ad-hoc networks (fanets): A survey. Ad Hoc Networks, 11(3):1254--1270, 2013.
[4]
Sanjit Biswas and Robert Morris. Opportunistic routing in multi-hop wireless networks. ACM SIGCOMM Computer Communication Review, 34(1):69--74, 2004.
[5]
Emmanuel J Candès, Justin Romberg, and Terence Tao. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory, 52, 2006.
[6]
Rui Castro, Mark Coates, Gang Liang, Robert Nowak, and Bin Yu. Network tomography: Recent developments. 2004.
[7]
Szymon Chachulski, Michael Jennings, Sachin Katti, and Dina Katabi. Trading structure for randomness in wireless opportunistic routing. ACM SIGCOMM Computer Communication Review, 37(4):169--180, 2007.
[8]
Nessrine Chakchouk. A survey on opportunistic routing in wireless communication networks. IEEE Communications Surveys & Tutorials, 17(4):2214--2241, 2015.
[9]
Cho-Chun Chiu and Ting He. Stealthy dgos attack against network tomography: The role of active measurements. IEEE Transactions on Network Science and Engineering, 8(2):1745--1758, 2021.
[10]
Cho-Chun Chiu and Ting He. Stealthy dgos attack: Degrading of service under the watch of network tomography. IEEE/ACM Transactions on Networking, 29(3):1294-- 1307, 2021.
[11]
Xiangrui Fan, Wenlong Cai, and Jinyong Lin. A survey of routing protocols for highly dynamic mobile ad hoc networks. In 2017 IEEE 17th International Conference on Communication Technology (ICCT), pages 1412--1417. IEEE, 2017.
[12]
XiaoBo Fan and Xingming Li. Network tomography via sparse bayesian learning. IEEE Communications Letters, 21(4):781--784, 2017.
[13]
Mohammad Hamed Firooz and Sumit Roy. Link delay estimation via expander graphs. IEEE Trans. Commun., 62:170--180, 2014.
[14]
Manjesh K Hanawal, Diep N Nguyen, and Marwan Krunz. Jamming attack on in-band full-duplex communications: Detection and countermeasures. In IEEE INFOCOM, 2016.
[15]
Ting He. Distributed link anomaly detection via partial network tomography. 2018.
[16]
Tao Hou, Zhe Qu, Tao Wang, Zhuo Lu, and Yao Liu. Proto: Proactive topology obfuscation against adversarial network topology inference. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pages 1598--1607. IEEE, 2020.
[17]
Yiyi Huang, Nick Feamster, and Renata Teixeira. Practical issues with using network tomography for fault diagnosis. ACM SIGCOMM Computer Communication Review, 38(5):53--58, 2008.
[18]
Amani Ibraheem, Zhengguo Sheng, George Parisis, and Daxin Tian. Neural network based partial tomography for in-vehicle network monitoring. In 2021 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1--6. IEEE, 2021.
[19]
Amani Ibraheem, Zhengguo Sheng, George Parisis, Jianshan Zhou, and Daxin Tian. Internal network monitoring with dnn and network tomography for invehicle networks. In 2022 IEEE International Conference on Unmanned Systems (ICUS), pages 928--933. IEEE, 2022.
[20]
Grigorios Kakkavas, Despoina Gkatzioura, Vasileios Karyotis, and Symeon Papavassiliou. A review of advanced algebraic approaches enabling network tomography for future network infrastructures. Future Internet, 12(2):20, 2020.
[21]
Hiroyuki Kasai, Wolfgang Kellerer, and Martin Kleinsteuber. Network volume anomaly detection and identification in large-scale networks based on online time-structured traffic tensor tracking. IEEE Trans. Netw. Service Manag., 13, 2016.
[22]
Mohammad Shoeb Saeed Khan. Network tomography application in mobile ad-hoc networks. University of Louisville, 2013.
[23]
Demeke Shumeye Lakew, Umar Sa'ad, Nhu-Ngoc Dao, Woongsoo Na, and Sungrae Cho. Routing in flying ad hoc networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2):1071--1120, 2020.
[24]
Fengyin Li, Pei Ren, Guoyu Yang, Yuhong Sun, Yilei Wang, Yanli Wang, Siyuan Li, and Huiyu Zhou. An efficient anonymous communication scheme to protect the privacy of the source node location in the internet of things. Security and Communication Networks, 2021:1--16, 2021.
[25]
Yimei Li and Yao Liang. Compressed sensing in multi-hop large-scale wireless sensor networks based on routing topology tomography. IEEE Access, 6:27637-- 27650, 2018.
[26]
Yongjun Li, Wandong Cai, Guangli Tian, and Wei Wang. Loss tomography in wireless sensor network using gibbs sampling. In Wireless Sensor Networks: 4th European Conference, EWSN 2007, Delft, The Netherlands, January 29--31, 2007. Proceedings 4, pages 150--162. Springer, 2007.
[27]
Yunzhong Liu, Rui Zhang, Jing Shi, and Yanchao Zhang. Traffic inference in anonymous manets. In IEEE SECON, 2010.
[28]
Zhuo Lu and Cliff Wang. Network anti-inference: A fundamental perspective on proactive strategies to counter flow inference. In IEEE INFOCOM, 2015.
[29]
Zhuo Lu and Cliff Wang. Enabling network anti-inference via proactive strategies: A fundamental perspective. IEEE/ACM Transactions on Networking, 25(1):43--55, 2016.
[30]
Liang Ma, Ting He, Kin K Leung, Ananthram Swami, and Don Towsley. Identifiability of link metrics based on end-to-end path measurements. In ACM IMC, 2013.
[31]
Liang Ma, Ting He, Kin K Leung, Don Towsley, and Ananthram Swami. Efficient identification of additive link metrics via network tomography. In 2013 IEEE 33rd International Conference on Distributed Computing Systems, pages 581--590. IEEE, 2013.
[32]
Liang Ma, Ziyao Zhang, and Mudhakar Srivatsa. Neural network tomography. arXiv preprint arXiv:2001.02942, 2020.
[33]
Morteza Mardani and Georgios B Giannakis. Estimating traffic and anomaly maps via network tomography. IEEE/ACM Trans. Netw., 24, 2016.
[34]
Takahiro Matsuda, Masaaki Nagahara, and Kazunori Hayashi. Link quality classifier with compressed sensing based on\ell_1-\ell_2 optimization. IEEE Communications Letters, 15(10):1117--1119, 2011.
[35]
Lu Mei-Hsuan, Steenkiste Peter, and Chen Tsuhan. Design, implementation and evaluation of an efficient opportunistic retransmission protocol. Proc. Of IEEE MobiCom, Beijing, China, 2009.
[36]
Mathew Penrose. Random geometric graphs, volume 5. OUP Oxford, 2003.
[37]
Ippokratis Sartzetakis and Emmanouel Varvarigos. Machine learning network tomography with partial topology knowledge and dynamic routing. In GLOBECOM 2022--2022 IEEE Global Communications Conference, pages 4922--4927. IEEE, 2022.
[38]
Anirvan M Sengupta and Partha P Mitra. Distributions of singular values for some random matrices. Physical Review E, 60, 1999.
[39]
Rahul C Shah, Sven Wietholter, and Adam Wolisz. Modeling and analysis of opportunistic routing in low traffic scenarios. In Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt'05), pages 294--304. IEEE, 2005.
[40]
Rajinder Singh and Satish Kumar. A comparative study of various wireless network monitoring tools. In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), pages 379--384. IEEE, 2018.
[41]
Paul Syverson, R Dingledine, and N Mathewson. Tor: The secondgeneration onion router. In USENIX Security, 2004.
[42]
Yehuda Vardi. Network tomography: Estimating source-destination traffic intensities from link data. Journal of the American statistical association, 91(433):365--377, 1996.
[43]
Wei Wang, Huiran Wang, Beizhan Wang, Yaping Wang, and Jiajun Wang. Energyaware and self-adaptive anomaly detection scheme based on network tomography in mobile ad hoc networks. Information Sciences, 220:580--602, 2013.
[44]
Zehua Wang, Yuanzhu Chen, and Cheng Li. Corman: A novel cooperative opportunistic routing scheme in mobile ad hoc networks. IEEE journal on selected areas in communications, 30(2):289--296, 2012.
[45]
Chung-Kai Yu, Kwang-Cheng Chen, and Shin-Ming Cheng. Cognitive radio network tomography. IEEE Trans. Veh. Technol., 59, 2010.
[46]
Zhenghao Zhang and Avishek Mukherjee. Friendly channel-oblivious jamming with error amplification for wireless networks. In IEEE INFOCOM, 2016.
[47]
Zhiyong Zhang, Ovidiu Mara, and Katerina Argyraki. Network neutrality inference. In ACM SIGCOMM, 2014.
[48]
Jerry Zhao, Ramesh Govindan, and Deborah Estrin. Sensor network tomography: Monitoring wireless sensor networks. ACM SIGCOMM Computer Communication Review, 32(1):64--64, 2002.
[49]
Shangqing Zhao, Zhuo Lu, and Cliff Wang. When seeing isn't believing: On feasibility and detectability of scapegoating in network tomography. In IEEE ICDCS, 2017.
[50]
Shangqing Zhao, Zhuo Lu, and Cliff Wang. How can randomized routing protocols hide flow information in wireless networks? IEEE Transactions on Wireless Communications, 19(11):7224--7236, 2020.
[51]
Shangqing Zhao, Zhuo Lu, and Cliff Wang. Measurement integrity attacks against network tomography: Feasibility and defense. IEEE Transactions on Dependable and Secure Computing, 18:2617--2630, Nov. 2021.
[52]
Zhonghua Zhao, Wei Huangfu, and Linmin Sun. Nssn: A network monitoring and packet sniffing tool for wireless sensor networks. In 2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC), pages 537--542. IEEE, 2012.
[53]
Zhongliang Zhao, Denis Rosário, Torsten Braun, Eduardo Cerqueira, Hongli Xu, and Liusheng Huang. Topology and link quality-aware geographical opportunistic routing in wireless ad-hoc networks. In 2013 9th international wireless communications and mobile computing conference (IWCMC), pages 1522--1527. IEEE, 2013.
[54]
Lan Zhuo, Yutong Li, Jun Deng, and Hao Wang. An anonymous communication method for wireless sensor networks based on bilinear pairings. In 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT, pages 517--525. IEEE, 2020.
Index Terms
- MMP: A Dynamic Routing Protocol Design to Proactively Defend against Wireless Network Inference Attacks
Recommendations
An Approach Used In Wireless Network to Detect Denial of Service Attack
WIR '16: Proceedings of the ACM Symposium on Women in Research 2016The shared behavior of wireless network makes it more vulnerable to Denial of Service attack. A Denial of Service (DOS) attack is a planned attempt to make resources unable to the legitimate client. These attacks can easily launch by perpetrator ...
Inference attacks against trust-based onion routing: Trust degree to the rescue
Trust-based onion routing enhances anonymity protection by means of constructing onion circuits using trust-based routers. However, attackers who have the knowledge of a priori trust distributions are still capable of largely reducing the anonymity ...
Comments
Information & Contributors
Information
Published In
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 November 2023
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
CCS '23
Sponsor:
CCS '23: ACM SIGSAC Conference on Computer and Communications Security
November 26, 2023
Copenhagen, Denmark
Acceptance Rates
Overall Acceptance Rate 40 of 92 submissions, 43%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 175Total Downloads
- Downloads (Last 12 months)175
- Downloads (Last 6 weeks)18
Reflects downloads up to 12 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in