Mobi-Watchdog: You can steal, but you can't run!

Proceedings of the 2nd ACM Conference on Wireless Network Security, WiSec'09

Volumn , Issue , 2009, Pages 139-150

  Yan, Guanhua ^a   Eidenbenz, Stephan ^a   Sun, Bo ^b  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b Lamar University   (United States)

Author keywords

Anomaly detection; HHMM; Mobility; Wireless networks

Indexed keywords

ANOMALY DETECTION; BAUM-WELCH ALGORITHMS; CELL PHONE; CLUSTERING TECHNIQUES; COMPUTATIONAL COSTS; COMPUTATIONAL OVERHEADS; FALSE ALARM RATE; HHMM; HIERARCHICAL HIDDEN MARKOV MODEL; HIGH PROBABILITY; HIGH-LEVEL STRUCTURE; MOBILITY; TIME COMPLEXITY;

CELL MEMBRANES; HIDDEN MARKOV MODELS; INTERNET; LAPTOP COMPUTERS; LOCOMOTIVES; MOBILE COMPUTING; MOBILE DEVICES; NETWORK SECURITY; OBJECT RECOGNITION; PORTABLE EQUIPMENT; RAILROAD CARS;

WIRELESS NETWORKS;

EID: 70349149823     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1514274.1514295     Document Type: Conference Paper

References (20)

1. S. V. Dongen. Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, May 2000.
2. A. J. Enright, S. V. Dongen, and C. A. Ouzounis. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research, 30(7), 2002.
3. F. Erbas, K. Kyamakya, J. Steuer, and K. Jobmann. On the user profiles and the prediction of user movements in wireless networks. In Proceedings of IEEE PIMRC, 2002.
4. S. Fine, Y. Singer, and N. Tishby. The hierarchical hidden Markov model: Analysis and applications. Machine Learning, 32, 1998.
5. M. C. González, C. A. Hidalgo, and A.-L. Barabási. Understanding individual human mobility patterns. Nature, 2008.
6. A. Jardosh, E. M. Belding-Royer, K. C. Almeroth, and S. Suri. Towards realistic models for mobile ad hoc networks. In Proc. of MobiCom, 2003.
7. J. Kim. Realistic mobility modeling and simulation for mobile wireless network in urban environments. Master's thesis, University of Deleware, 2005.
8. Lin Liao and Dieter F. Kautz. Extracting places and activities from gps traces using hierarchical conditional random fields. Artificial Intelligence, 2007.
9. http://www.washingtonpost.com/wp-dyn/content/article/2005/07/24/ AR20050727.401135.html.
10. K. Murphy and M. Paskin. Linear time inference in hierarchical HMMs. In Advances in Neural Information Processing Systems 13. MIT Press, Cambridge, MA, 2002.
11. M. Musolesi, S. Hailes, and C. Mascolo. An ad hoc mobility model founded on social newtork theory. In Proceedings of MSWiM'04, 2004.
12. L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proc. of the IEEE, February 1989.
13. Reality mining project. http://reality.media.mit.edu/.
14. S. Schonfelder. Some notes on space, location and travel behavior. In Swiss Transport Research Conference, Monte Verita, Ascona, 2001.
15. J. Scourias and T. Kunz. An activity-based mobility model and location management simulation framework. In Proceedings of MSWiM '99, New York, NY, USA, 1999.
16. B. Sun, Y. Xiao, and R. Wang. Detection of fraudulent usage in wireless networks. IEEE Trans. on Vehicular Technology, 56(6), 2007.
17. B. Sun, F. Yu, K. Wu, Y. Xiao, and V. C. M. Leung. Enhancing security using mobility-based anomaly detection in cellular mobile networks. IEEE Trans. on Vehicular Technology, 55(3), 2006.
18. http://cbs5.com/consumer/wireless.runaround.2.451677.html.
19. nd SIGHAN workshop on Chinese language processing, 2003.
20. J. Zhang. Handbook of Wireless Networks and Mobile Computing, chapter Location Management in Cellular Networks. John Wiley & Sons, Inc., 2002.