The Community for Technology Leaders
RSS Icon
Issue No.03 - March (2013 vol.12)
pp: 434-446
U. Paul , Comput. Sci. Dept., Stony Brook Univ., Stony Brook, NY, USA
A. Kashyap , Symantec Res. Labs., San Jose, CA, USA
R. Maheshwari , Akamai Technol., Somerville, MA, USA
S. R. Das , Comput. Sci. Dept., Stony Brook Univ., Stony Brook, NY, USA
We present a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This tool does not require any controlled experiments, injection of probe traffic in the network, or even access to the network nodes. Our approach requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed using a machine learning approach to infer the carrier-sense relationship between network nodes. This coupled with an estimation of collision probabilities helps us to deduce the interference relationships. We also demonstrate an important application of this tool-detection of selfish carrier-sense behavior. This is based on identifying any asymmetry in carrier-sense behavior between node pairs and finding multiple witnesses to raise confidence. We evaluate the effectiveness of the tool for both the applications using extensive experiments and simulation. Experimental and simulation results demonstrate that the proposed approach of estimating interference relations is significantly more accurate than simpler heuristics and quite competitive with active measurements. We also validate the approach in a real Wireless LAN environment. Evaluations using a real testbed as well as ns2 simulation studies demonstrate excellent detection ability of the selfish behavior. On the other hand, the metric of selfishness used to estimate selfish behavior matches closely with actual degree of selfishness observed.
wireless LAN, interference suppression, learning (artificial intelligence), probability, telecommunication computing, telecommunication traffic, wireless LAN, passive measurement, interference measurement, WiFi network, misbehavior detection, passive monitoring, wireless traffic, multiple sniffers, machine learning, carrier-sense relationship, collision probabilities, selfish carrier-sense behavior, Interference, Hidden Markov models, Markov processes, IEEE 802.11 Standards, Wireless communication, Sensors, Monitoring, interference, 802.11 protocol, hidden Markov model, MAC layer misbehavior
U. Paul, A. Kashyap, R. Maheshwari, S. R. Das, "Passive Measurement of Interference in WiFi Networks with Application in Misbehavior Detection", IEEE Transactions on Mobile Computing, vol.12, no. 3, pp. 434-446, March 2013, doi:10.1109/TMC.2011.259
[1] A.P. Jardosh, K.N. Ramachandran, K.C. Almeroth, and E.M. Belding-Royer, "Understanding Congestion in IEEE 802.11b Wireless Networks," Proc. ACM SIGCOMM, 2005.
[2] M. Rodrig, C. Reis, R. Mahajan, D. Wetherall, and J. Zahorjan, "Measurement-Based Characterization of 802.11 in a Hotspot Setting," Proc. ACM SIGCOMM, 2005.
[3] A. Kashyap, U. Paul, and S.R. Das, "Deconstructing Interference Relations in WiFi Networks," Proc. IEEE Seventh Comm. Soc. Conf. Sensor Mesh and Ad Hoc Comm. and Networks (SECON), 2010.
[4] U. Paul, S.R. Das, and R. Maheshwari, "Detecting Selfish Carrier-Sense Behavior in Wifi Networks by Passive Monitoring," Proc. IEEE/IFIP Int'l Conf. Dependable Systems and Networks (DSN), 2010.
[5] "AirMagnet WiFi Analyzer," productswifi_analyzer , 2012.
[6] "AirPatrol's Wireless Threat Management Solutions," http:/, 2012.
[7] P. Bahl et al., "DAIR: A Framework for Troubleshooting Enterprise Wireless Networks Using Desktop Infrastructure," Proc. ACM HotNets-IV, 2005.
[8] P. Bahl et al., "Enhancing the Security of Corporate Wi-Fi Networks Using DAIR," Proc. ACM/USENIX Mobile Systems, Applications, and Services (MobiSys), 2006.
[9] Y.-C. Cheng, J. Bellardo, P. Benkö, A.C. Snoeren, G.M. Voelker, and S. Savage, "Jigsaw: Solving the Puzzle of Enterprise 802.11 Analysis," Proc. ACM SIGCOMM, 2006.
[10] R. Mahajan, M. Rodrig, D. Wetherall, and J. Zahorjan, "Analyzing the MAC-Level Behavior of Wireless Networks in the Wild," Proc. ACM SIGCOMM, 2006.
[11] K. Pelechrinis, G. Yan, S. Eidenbenz, and S.V. Krishnamurthy, "Detecting Selfish Exploitation of Carrier Sensing in 802.11 Networks," Proc. IEEE INFOCOM, 2009.
[12] J. Yeo, M. Youssef, and A. Agrawala, "A Framework for Wireless Lan Monitoring and its Applications," Proc. Third ACM Workshop Wireless Security (WiSe), 2004.
[13] J. Padhye, S. Agarwal, V. Padmanabhan, L. Qiu, A. Rao, and B. Zill, "Estimation of Link Interference in Static Multi-Hop Wireless Networks," Proc. Internet Measurement Conf. (IMC), 2005.
[14] C. Reis, R. Mahajan, M. Rodrig, D. Wetherall, and J. Zahorjan, "Measurement-Based Models of Delivery and Interference in Static Wireless Networks," Proc. ACM SIGCOMM, 2006.
[15] A. Kashyap, S. Ganguly, and S.R. Das, "A Measurement-Based Approach to Modeling Link Capacity in 802.11-Based Wireless Networks," Proc. ACM MobiCom, 2007.
[16] L. Qiu, Y. Zhang, F. Wang, M.K. Han, and R. Mahajan, "A General Model of Wireless Interference," Proc. ACM MobiCom, 2007.
[17] K. Jamieson, B. Hull, A.K. Miu, and H. Balakrishnan, "Understanding the Real-World Performance of Carrier Sense," Proc. ACM SIGCOMM Workshop Experimental Approaches to Wireless Network Design and Analysis (E-WIND), Aug. 2005.
[18] H. Chang, V. Misra, and D. Rubenstein, "A General Model and Analysis of Physical Layer Capture in 802.11 Networks," Proc. IEEE INFOCOM, 2006.
[19] S. Das, D. Koutsonikolas, Y. Hu, and D. Peroulis, "Characterizing Multi-Way Interference in Wireless Mesh Networks," Proc. First Int'l Workshop Wireless Network Testbeds, Experimental Evaluation and Characterization (WINTECH), 2005.
[20] E. Magistretti, O. Gurewitz, and E. Knightly, "Inferring and Mitigating a Link's Hindering Transmissions in Managed 802.11 Wireless Networks," Proc. ACM MobiCom, 2010.
[21] M. Cagalj, S. Ganeriwal, I. Aad, and J.-P. Hubaux, "On Selfish Behavior in CSMA/CA Networks," Proc. IEEE INFOCOM, 2005.
[22] S. Radosavac, J.S. Baras, and I. Koutsopoulos, "A Framework for Mac Protocol Misbehavior Detection in Wireless," Proc. ACM Workshop Wireless Security, 2005.
[23] J. Tang, Y. Cheng, Y. Hao, and C. Zhou, "Real-Time Detection of Selfish Behavior in IEEE 802.11 Wireless Networks," Proc. IEEE 72nd Vehicular Technology Conf. Fall (VTC-Fall), 2010.
[24] P. Kyasanur and N. Vaidya, "Detection and Handling of Mac Layer Misbehavior in Wireless Networks," Proc. IEEE Int'l Conf. Dependable Systems and Networks (DSN), 2003.
[25] M. Raya, J.-P. Hubaux, and I. Aad, "Domino: A System to Detect Greedy Behavior in IEEE 802.11 Hotspots," Proc. ACM Second Int'l Conf. Mobile Systems, Applications, and Services (MobiSys), 2004.
[26] P. Gupta and P.R. Kumar, "The Capacity of Wireless Networks," IEEE Trans. Information Theory, vol. 46, no. 2, pp. 388-404, Mar. 2000.
[27] L.R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Readings in Speech Recognition, pp. 267-296, Morgan Kaufmann, 1990.
[28] A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc. Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
[29] L.E. Baum and J.A. Eagon, "An Inequality with Applications to Statistical Estimation for Probabilistic Functions of Markov Processes and to a Model for Ecology," Bull. Am. Math. Soc., vol. 73, pp. 360-363, 1967.
[30] G. Bianchi, "Performance Analysis of the IEEE 802.11 Distributed Coordination Function," IEEE J. Selected Areas in Comm., vol. 18, no. 3, pp. 535-547, 2000.
[31] S.E. Levinson, L.R. Rabiner, and M.M. Sondhi, "An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition," Bell System Technical J., vol. 62, no. 4, pp. 1035-1074, 1983.
[32] S. Rayanchu, A. Mishra, D. Agrawal, S. Saha, and S. Banerjee, "Diagnosing Wireless Packet Losses in 802.11: Separating Collision from Weak Signal," Proc. IEEE INFOCOM, 2008.
[33] A. Kashyap, S.R. Das, and S. Ganguly, "Measurement-Based Approaches for Accurate Simulation of 802.11-Based Wireless Networks," Proc. ACM 11th Int'l Symp. Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), 2008.
[34] M. Rodrig, C. Reis, R. Mahajan, D. Wetherall, J. Zahorjan, and E. Lazowska, "CRAWDAD Data Set uw/sigcomm2004," , 2012.
[35] K. Chebrolu, B. Raman, and S. Sen, "Long-Distance 802.11b Links: Performance Measurements and Experience," Proc. ACM MobiCom, 2006.
[36] T.S. Rappaport, Wireless Comm.: Principles and Practice. IEEE Press, 1996.
39 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool