Skip to main content
SpringerLink
Log in

TTLed Random Walks for Collaborative Monitoring in Mobile and Social Networks

  • Chapter
  • First Online:
Handbook of Optimization in Complex Networks

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 57))

Abstract

Complex network and complex systems research has been proven to have great implications in practice in many scopes including Social Networks, Biology, Disease Propagation, and Information Security. One can use complex network theory to optimize resource locations and optimize actions. Randomly constructed graphs and probabilistic arguments lead to important conclusions with a possible great social and financial influence. Security in online social networks has recently become a major issue for network designers and operators. Being “open” in their nature and offering users the ability to compose and share information, such networks may involuntarily be used as an infection platform by viruses and other kinds of malicious software. This is specifically true for mobile social networks, that allow their users to download millions of applications created by various individual programers, some of which may be malicious or flawed. In order to detect that an application is malicious, monitoring its operation in a real environment for a significant period of time is often required. As the computation and power resources of mobile devices are very limited, a single device can monitor only a limited number of potentially malicious applications locally. In this work, we propose an efficient collaborative monitoring scheme that harnesses the collective resources of many mobile devices, generating a “vaccination”-like effect in the network. We suggest a new local information flooding algorithm called Time-to-Live Probabilistic Propagation (TPP). The algorithm is implemented in any mobile device, periodically monitors one or more applications and reports its conclusions to a small number of other mobile devices, who then propagate this information onward, whereas each message has a predefined “Time-to-Live” (TTL) counter. The algorithm is analyzed, and is shown to outperform the existing state of the art information propagation algorithms, in terms of convergence time as well as network overhead. We then show both analytically and experimentally that implementing the proposed algorithm significantly reduces the number of infected mobile devices. Finally, we analytically prove that the algorithm is tolerant to the presence of adversarial agents that inject false information into the system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We assume that interference in the messages’ content, or generation of messages using false identity are impossible, due to, say, the use of cryptographic means.

  2. 2.

    This will later come into effect when messages will be sent between the network’s members, at which case the selection of “an arbitrary network member” can be assumed to be purely random.

  3. 3.

    The intuition behind this assumption is as follows: we aspire that the number of messages each device is asked to send upon discovering a new malicious application is kept to a minimum. As the value of P N is required to be greater than \(\frac{\ln n} {n}\) in order to guarantee connectivity [23], it is safe to assume that \({P}_{N} = O\left (\frac{\ln n} {n}\right )\). Notice that under some assumptions, a connected pseudo-random graph can still be generated, such that \({p}_{N} = O( \frac{1} {n})\) (see for example [21]). However, as we are interested in demonstrating the result for any random graph G(n, p), this lower bound of p N is still mentioned. In addition, we later show that timeout ≈ ​ O(logn). It is also safe to assume that N ≈ Ω(lnn) and that \({P}_{\mathrm{MAX}} \approx O\left ( \frac{1} {\ln n}\right )\). This assumption is later discussed in great details.

  4. 4.

    See Sect. 17.6 for more details.

  5. 5.

    Note that the number of malicious applications does not influence the completion time of algorithm, as monitoring and notification is done in parallel. The number of message, however, grows linearly with the number of malicious applications.

References

  1. Mcafee mobile security report 2008. Tech. rep. (2008). http://www.mcafee.com/us/resources/reports/rp-mobile-security-2008.pdf

  2. Mcafee mobile security report 2009. Tech. rep. (2009). http://www.mcafee.com/us/resources/reports/rp-mobile-security-2009.pdf

  3. Adamic, L.A., Lukose, R.M., Puniyani, A.R., Huberman, B.A.: Search in power-law networks. Phys. Rev. E 64(4), 046,135 (2001). DOI 10.1103/PhysRevE.64.046135

    Google Scholar 

  4. Altshuler, Y., Bruckstein, A.M.: Static and expanding grid coverage with ant robots: Complexity results. Theor. Comput. Sci. 412(35), 4661–4674 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. Altshuler, Y., Yanovsky, V., Bruckstein, A., Wagner, I.: Efficient cooperative search of smart targets using uav swarms. ROBOTICA 26, 551–557 (2008)

    Article  Google Scholar 

  6. Angluin, D., Aspnes, J., Eisenstat, D.: A simple population protocol for fast robust approximate majority. Dist. Comp. 21, 87–102 (2008)

    Article  Google Scholar 

  7. Apap, F., Honig, A., Hershkop, S., Eskin, E., Stolfo, S.: Detecting Malicious Software by Monitoring Anomalous Windows Registry Accesses. Recent Advances in Intrusion Detection, pp. 36–53. Springer Berlin Heidelberg (2002)

    Google Scholar 

  8. Aspnes, J., Ruppert, E.: An Introduction to Population Protocols. Middleware for Network Eccentric and Mobile Applications, pp. 97–120. Springer Berlin Heidelberg (2009)

    Google Scholar 

  9. Bailey, N.: The Mathematical Theory of Infectious Diseases and its Applications (second edition). Hafner Press (1975)

    Google Scholar 

  10. Barak, B., Halevi, S., Herzberg, A., Naor, D.: Clock synchronization with faults and recoveries (extended abstract). In: PODC ’00: Proceedings of the nineteenth annual ACM symposium on Principles of distributed computing, pp. 133–142. ACM, New York, NY, USA (2000). DOI http://doi.acm.org/10.1145/343477.343534

  11. Batalin, M., Sukhatme, G.: Spreading out: A local approach to multi-robot coverage. In: 6th International IEEE Symposium on Distributed Autonomous Robotics Systems, (IEEE) (2002)

    Google Scholar 

  12. Cagalj, M., Hubaux, J., Enz, C.: Minimum-energy broadcast in all-wireless networks: Np-completness and distribution issues. In: The Annual International Conference on Mobile Computing and Networking (MOBICOM), ACM (Atlanta Georgia) (2002)

    Google Scholar 

  13. Mobile Broadband Growth report: HSPA/HSPA+ operator success stories worldwide, trends, forecasts, GSA (the Global mobile Suppliers Association) (2010)

    Google Scholar 

  14. Castelluccia, C., Jarecki, S., Kim, J., Tsudik, G.: Secure acknowledgement aggregation and multisignatures with limited robustness. Computer Networks 50(10), 1639–1652 (2006)

    Article  MATH  Google Scholar 

  15. Choy, M., Singh, A.K.: Efficient fault-tolerant algorithms for distributed resource allocation. ACM Trans. Program. Lang. Syst. 17(3), 535–559 (1995). DOI http://doi.acm.org/10.1145/203095.203101

    Google Scholar 

  16. Chung, F., Lu, L.: The diameter of sparse random graphs. Advances in Applied Mathematics 26, 257–279 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  17. Crisostomo, S., Barros, J., Bettstetter, C.: Flooding the network: Multipoint relays versus network coding. In: 4th IEEE Intl. Conference on Circuits and Systems for Communications (ICCSC). IEEE (Shanghai, China), pp. 119–124 (2008)

    Google Scholar 

  18. Demers, A., Greene, D., Hauser, C., Irish, W., Larson, J., Shenker, S., Sturgis, H., Swinehart, D., Terry, D.: Epidemic algorithms for replicated database maintenance. In: In Proc. of the Sixth ACM Symposium on Principles of Distributed Computing, (Vancouver, Canada), pp. 1–12 (1987)

    Google Scholar 

  19. D.F. Zucker M. Uematsu, T.K.: Markup-based smartphone user interface using the web browser engine. In: Proceedings XTech. IDEAlliance, Amsterdam, pp. 25–27 (2005)

    Google Scholar 

  20. Dolev, S., Schiller, E., Welch, J.: Random walk for self-stabilizing group communication in ad hoc networks. IEEE Transactions on Mobile Computing, Switzerland 5, 893–905 (2006)

    Article  Google Scholar 

  21. Dolev, S., Tzachar, N.: Spanders: Distributed spanning expanders. In: Proceedings ACM SCS (ACM), Switzerland (2010)

    Google Scholar 

  22. Erdos, P., Renyi, A.: On random graphs. Publ. Math. Debrecen 6, 290–291 (1959)

    MathSciNet  Google Scholar 

  23. Erdos, P., Renyi, A.: On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5, 17–61 (1960)

    MathSciNet  Google Scholar 

  24. Fragouli, C., Widmer, J., Boudec, J.L.: A network coding approach to energy efficient broadcasting: from theory to practice. In: The 25th IEEE International Conference on Computer Communications (INFOCOM2006). IEEE, Barcelona, pp. 1–11 (2006)

    Google Scholar 

  25. Ganesa, D., Krishnamachari, B., Woo, A., Culler, D., Estrin, D., Wicker, S.: An empirical study of epidemic algorithms in large scale multihop wireless networks – technical report ucla/csd-tr 02-0013. Technical report, UCLA Computer Science (2002)

    Google Scholar 

  26. Gemmel, P.: An introduction to threshold cryptography. CryptoBytes, RSA Labs, pp. 7–12 (1997)

    Google Scholar 

  27. GetJar: Getjar statistics (2010)

    Google Scholar 

  28. Golding, R., Long, D., Wilkes, J.: The refdbms distributed bibliographic database system. In: In Proc. of USENIX. USENIX (the advanced computer systems association), Boston MA, pp. 47–62 (1994)

    Google Scholar 

  29. Haas, Z., Halpern, J., Li, L.: Gossip-based ad-hoc routing. IEEE/ACM Transactions of networks 14(3), 479–491 (2006)

    Article  Google Scholar 

  30. Hypponen, M.: Malware goes mobile. Sci. American 295, 70 (2006)

    Google Scholar 

  31. Hypponen, M.: State of cell phone malware in 2007. Technical report FSECURE (2007)

    Google Scholar 

  32. J. Cheng S. Wong, H.Y.S.L.: Smartsiren: Virus detection and alert for smartphones. In: In Proceedings of the Fifth ACM International Conference on Mobile Systems Applications and Services (MOBISYS). ACM, Puerto Rico, pp. 258–271 (2007)

    Google Scholar 

  33. Jacoby, G., Davis, N.: Battery-based intrusion detection. In: Proceedings of the IEEE Global Telecommunications Conference, GLOBECOM04. IEEE, Dallas Texas, vol. 4, pp. 2250–2255 (2004)

    Google Scholar 

  34. Jacquet, P., Laouiti, A., Minet, P., Viennot, L.: Performance analysis of olsr multipoint relay flooding in two ad-hoc wireless network models. technical report 4260. Technical report, INRIA (2001)

    Google Scholar 

  35. Jonasson, J., Schramm, O.: On the cover time of planar graphs. Electron. Comm. Probab. 5, 85–90 (electronic) (2000)

    Google Scholar 

  36. Kim, H., Smith, J., Shin, K.G.: Detecting energy-greedy anomalies and mobile malware variants. In: MobiSys ’08: Proceeding of the 6th international conference on Mobile systems, applications, and services, pp. 239–252. ACM, New York, NY, USA (2008). DOI http://doi.acm.org/10.1145/1378600.1378627

  37. Kleinberg, J.: The wireless epidemic. Nature 449, 287–288 (2007)

    Google Scholar 

  38. Koenig, S., Liu, Y.: Terrain coverage with ant robots: A simulation study. In: The International Conference on Autonomous Agents (AGENTS). (ACM), Montreol Canada 600–607 (2001)

    Google Scholar 

  39. Koenig, S., Szymanski, B., Liu, Y.: Efficient and inefficient ant coverage methods. Annals of Mathematics and Artificial Intelligence 31, 41–76 (2001)

    Article  Google Scholar 

  40. Kong, C., Peng, N., Rekleitis, I.: Distributed coverage with multi-robot system. In: IEEE International Conference on Robotics and Automation (ICRA). IEEE Orlando Florida, (2006)

    Google Scholar 

  41. Korf, R.: Real-time heuristic search. Artificial Intelligence 42, 189–211 (1990)

    MATH  Google Scholar 

  42. Lim, H., Kim, C.: Multicast tree construction and flooding in wireless ad hoc networks. In: In Proceedings of the ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM). ACM, Bodrum Turkey (2000)

    Google Scholar 

  43. Liu, D., Ning, P., Liu, A., Wang, C., Du, W.K.: Attack-resistant location estimation in wireless sensor networks. ACM Trans. Inf. Syst. Secur. 11(4), 1–39 (2008). DOI http://doi.acm.org/10.1145/1380564.1380570

  44. Lv, Q., Cao, P., Cohen, E., Li, K., Shenker, S.: Search and replication in unstructured peer-to-peer networks. In: ICS ’02: Proceedings of the 16th international conference on Supercomputing, pp. 84–95. ACM, New York, NY, USA (2002). DOI http://doi.acm.org/10.1145/514191.514206

  45. Moskovitch, R., Gus, I., Pluderman, S., Stopel, D., Glezer, C., Shahar, Y., Elovici, Y.: Detection of unknown computer worms activity based on computer behavior using data mining. In: CISDA 2007. IEEE Symposium on Computational Intelligence in Security and Defense Applications. IEEE, Honolulu, HI, USA, pp. 169–177 (2007)

    Google Scholar 

  46. Moskovitch, R., Pluderman, S., Gus, I., Stopel, D., Feher, C., Parmet, Y., Shahar, Y., Elovici, Y.: Host based intrusion detection using machine learning. In: 2007 IEEE Intelligence and Security Informatics. IEEE, ISI, New Jersey, pp. 107–114 (2007)

    Google Scholar 

  47. Mutz, D., Valeur, F., Vigna, G., Kruegel, C.: Anomalous system call detection. ACM Trans. Inf. Syst. Secur. 9(1), 61–93 (2006). DOI http://doi.acm.org/10.1145/1127345.1127348

    Google Scholar 

  48. Narasimha, M., Tsudik, G., Yi, J.H.: On the utility of distributed cryptography in p2p and manets: the case of membership control. In: Proceedings of the 11th IEEE International Conference on Network Protocols. IEEE, Atlanta Georgia, pp. 336–345 (2003)

    Google Scholar 

  49. Nash, D.C., Martin, T.L., Ha, D.S., Hsiao, M.S.: Towards an intrusion detection system for battery exhaustion attacks on mobile computing devices. Pervasive Computing and Communications Workshops, IEEE International Conference on. IEEE, PERCOM, Hawaii, 0, 141–145 (2005). DOI http://doi.ieeecomputersociety.org/10.1109/PERCOMW.2005.86

  50. Ni, S., Tseng, Y., Chen, Y., Sheu, J.: The broadcast storm problem in a mobile ad hoc network. In: In Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM), Seattle, Washington, pp. 151–162 (1999)

    Google Scholar 

  51. Peng, W., Lu, X.C.: On the reduction of broadcast redundancy in mobile ad hoc networks. In: MobiHoc ’00: Proceedings of the 1st ACM international symposium on Mobile ad hoc networking & computing, pp. 129–130. IEEE Press, Piscataway, NJ, USA (2000)

    Google Scholar 

  52. Polycarpou, M., Yang, Y., Passino, K.: A cooperative search framework for distributed agents. In: IEEE International Symposium on Intelligent Control (ISIC), Mexico City, pp. 1–6 (2001)

    Google Scholar 

  53. Qayyum, L., Laouiti, A.: Multipoint relaying for flooding broadcast messages in mobile wireless networks. In: Proceedings of HICSS. IEEE Hawaii, (2002)

    Google Scholar 

  54. Rekleitis, I., Lee-Shuey, V., Newz, A.P., Choset, H.: Limited communication, multi-robot team based coverage. In: IEEE International Conference on Robotics and Automation. IEEE, Barcelona (2004)

    Google Scholar 

  55. van Renesse, R.: Power-aware epidemics. Reliable Distributed Systems, IEEE Symposium on. IEEE Computer Society, Osaka Japan, 0, 358 (2002). DOI http://doi.ieeecomputersociety.org/10.1109/RELDIS.2002.1180210

  56. van Renesse, R., Birman, K.: Scalable management and data mining using astrolabe. In: In Proc. of the First International Workshop on Peer-to-Peer Systems (IPTPS02). Springer, Cambridge MA, (2002)

    Google Scholar 

  57. Sasson, Y., Cavin, D., Schiper, A.: Probabilistic broadcas for flooding in wireless mobile ad-hoc networks. In: Proceedings of IEEE Wireless communication and networks (WCNC). IEEE, New Orleans, Louisiana (2003)

    Google Scholar 

  58. Shevchenko, A.: An overview of mobile device security. Kaspersky Labs, (available at www.viruslist.com/en/analysis?pubid$=$170773606) (2005)

  59. Stojmenovic, I., Seddigh, M., Zunic, J.: Ahbp: An efficient broadcast protocol for mobile ad hoc networks. Journal of Computer Science and Technology 16(2), 114–125 (2001)

    Article  Google Scholar 

  60. Stojmenovic, I., Seddigh, M., Zunic, J.: Dominating sets and neighbor elimination-based broadcasting algorithms in wireless networks. IEEE Transactions on Parallel and Distributed Systems 13(1), 14–25 (2002). DOI http://doi.ieeecomputersociety.org/10.1109/71.980024

    Google Scholar 

  61. Stone, L.: Theory of Optimal Search. Academic Press, New York (1975)

    MATH  Google Scholar 

  62. Svennebring, J., Koenig, S.: Building terrain-covering ant robots: A feasibility study. Autonomous Robots 16(3), 313–332 (2004)

    Article  Google Scholar 

  63. Vogels, W., van Renesse, R., Birman, K.: The power of epidemics: robust communication for large-scale distributed systems. SIGCOMM Comput. Commun. Rev. 33(1), 131–135 (2003). DOI http://doi.acm.org/10.1145/774763.774784

  64. Wagner, I., Altshuler, Y., Yanovski, V., Bruckstein, A.: Cooperative cleaners: A study in ant robotics. The International Journal of Robotics Research (IJRR) 27(1), 127–151 (2008)

    Google Scholar 

  65. Wang, P., Gonzalez, M., Hidalgo, C., Barabasi, A.: Understanding the spreading patterns of mobile phone viruses. Science 324, 1071–1075 (2009)

    Article  Google Scholar 

  66. Williams, B., Camp, T.: Comparison of broadcasting techniques for mobile ad hoc networks. In: The Third ACM International Symposium on Mobile Ad Hoc Networking and Computing. Lausanne, Switzerland, pp. 9–11 (2002)

    Google Scholar 

  67. Zlot, R., Stentz, A., Dias, M., Thayer, S.: Multi-robot exploration controlled by a market economy. In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE, Washington DC (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Systems Engineering and Deutsche Telekom Laboratories, Ben Gurion University of the Negev, 653, Beer Sheva, 84105, Israel

    Yaniv Altshuler & Yuval Elovici

  2. MIT Media Laboratory, 77 Mass. Ave., E15, Cambridge, MA, 02139, USA

    Yaniv Altshuler

  3. Computer Science Department, Ben Gurion University of the Negev, 653, Beer Sheva, 84105, Israel

    Shlomi Dolev

Authors
  1. Yaniv Altshuler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shlomi Dolev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuval Elovici
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaniv Altshuler .

Editor information

Editors and Affiliations

  1. Science and Engineering, Department of Computer and Information, University of Florida, Gainesville, 3260, Florida, USA

    My T. Thai

  2. , Department of Industrial & Systems Engin, University of Florida, Weil Hall 401, Gainesville, 32611-6595, Florida, USA

    Panos M. Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Altshuler, Y., Dolev, S., Elovici, Y. (2012). TTLed Random Walks for Collaborative Monitoring in Mobile and Social Networks. In: Thai, M., Pardalos, P. (eds) Handbook of Optimization in Complex Networks. Springer Optimization and Its Applications(), vol 57. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-0754-6_17

Download citation

Publish with us

Policies and ethics

Search

Navigation