Skip to main content
SpringerLink
Log in

A Neural Network Model for Intrusion Detection Using a Game Theoretic Approach

  • Conference paper
  • First Online:
Advanced Informatics for Computing Research (ICAICR 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 712))

  • 841 Accesses

Abstract

The problem of intrusion detection in the computer networks is not new and various methodologies have been formulated to address the same. A game-theoretic representation was also formulated, using one of the oldest game playing techniques, the minimax algorithm to solve this problem. It exploited the adversary like situation between the intruder and the Intrusion Detection System (IDS) and the essence of this approach lies in the assumption that the intruder and the IDS have complete knowledge of the network and each other’s strategy. The solution for the intrusion detection problem via game theory gives the detection probability by which the IDS can detect the malicious packets on a given network when the probabilities with which the intruder sends the malicious packets on the various paths leading him to the target are known to the IDS. However, in the real world scenario, the role of the intruder and the IDS is dynamic, if the attack is detected or goes undetected the intruder tries to breach the network again with a different approach or the IDS tries to defend the network with a different strategy respectively. The next strategy for either of the two can be learnt by experience and thus, this paper, models an artificial neural network to represent this game-theoretic representation. The modeled neural network gives the detection probability of an attack by the IDS when the probabilities of sending malicious packets on the various paths leading the intruder to the target are given as an input pattern to the neural network.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Denning, D.E.: An intrusion-detection model. IEEE Trans. Softw. Eng. 2, 222–232 (1987). doi:10.1109/TSE.1987.232894

    Article  Google Scholar 

  2. Richard, A.K., Giovanni, V.: Intrusion detection: a brief history and overview. Computer 35, supl27–supl30 (2002). doi:10.1109/MC.2002.1012428

    Google Scholar 

  3. Chih, F.T., Yu, F.H., Chia, Y.L., Wei, Y.L.: Intrusion detection by machine learning: a review. Expert Syst. Appl. 36, 11994–12000 (2009). doi:10.1016/j.eswa.2009.05.029

    Article  Google Scholar 

  4. Wun, H.C., Sheng, H.H., Hwang, P.S.: Application of SVM and ANN for intrusion detection. Comput. Oper. Res. 32, 2617–2634 (2005). doi:10.1016/j.cor.2004.03.019

    Article  MATH  Google Scholar 

  5. Gang, W., Jinxing, H., Jian, M., Lihua, H.: A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37, 6225–6232 (2010). doi:10.1016/j.eswa.2010.02.102

    Article  Google Scholar 

  6. Mrutyunjaya, P., Manas, R.P.: Network intrusion detection using Naive Bayes. IJCSNS Int. J. Comput. Sci. Netw. Secur. 7, 258–263 (2007). https://pdfs.semanticscholar.org/1a5c/191da4aa733c80311ef4057c16dc899819cd.pdf

  7. Donald, E.K., Ronald, W.M.: An analysis of alpha beta pruning. Artif. Intell. 6, 293–326 (1975). doi:10.1016/0004-3702(75)90019-3

    Article  MathSciNet  MATH  Google Scholar 

  8. George, T.H., Gary, P., Stanley, S.: Chapter 7: path finding in AI. In: Algorithms in a Nutshell, pp. 213–217. Oreilly Media (2008)

    Google Scholar 

  9. Stockman, G.C.: A minimax algorithm better than alpha-beta? Artif. Intell. 12, 179–196 (1979). doi:10.1016/0004-3702(79)90016-X

    Article  MathSciNet  MATH  Google Scholar 

  10. Cameron, B.B., Edward, P., Daniel, W., Simon, M.L., Peter, I.C., Philipp, R., Stephen, T., Diego, P., Spyridon, S., Simon, C.: A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4, 1–43 (2012). doi:10.1109/TCIAIG.2012.2186810

    Article  Google Scholar 

  11. Hadi, O., Mona, M., Chadi, A., Mourad, D., Prabir, B.: Game theoretic models for detecting network intrusions. Comput. Commun. 31, 1934–1944 (2008). doi:10.1016/j.comcom.2007.12.028

    Article  Google Scholar 

  12. Michalski, R.S., Jaime, G.C., Tom, M.M.: Machine Learning: An Artificial Intelligence Approach. Springer Science & Business Media, Heidelberg (2013)

    MATH  Google Scholar 

  13. Yegnanarayana, B.: Chapter 1: basics of artificial neural networks. In: Artificial Neural Networks. PHI Learning Pvt. Ltd., pp. 15–39 (2009)

    Google Scholar 

  14. Chellapilla, K., David, B.F.: Evolving an expert checkers playing program without using human expertise. IEEE Trans. Evol. Comput. 5, 422–428 (2001). doi:10.1109/4235.942536

    Article  Google Scholar 

  15. David, S., Aja, H., Chris, J.M., Arthur, G., Laurent, S., George, V.D.D., Julian, S., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016)

    Article  Google Scholar 

  16. Andrew, N.L., Edward, G., Thomas, C.H.: Evolution of neural controllers for competitive game playing with teams of mobile robots. Robot. Auton. Syst. 46, 135–150 (2004). doi:10.1016/j.robot.2004.01.001

    Article  Google Scholar 

  17. Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.: Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 6, 442–449 (1991). doi:10.1109/59.76685

    Article  Google Scholar 

  18. Quan, H., Srinivasan, D., Khosravi, A.: Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans. Neural Netw. Learn. Syst. 25, 303–315 (2014). doi:10.1109/TNNLS.2013.2276053

    Article  Google Scholar 

  19. Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40, 5501–5506 (2013). doi:10.1016/j.eswa.2013.04.013

    Article  Google Scholar 

  20. Kristjanpoller, W., Minutolo, M.C.: Gold price volatility: a forecasting approach using the artificial neural Network–GARCH model. Expert Syst. Appl. 42, 7245–7251 (2015). doi:10.1016/j.eswa.2015.04.058

    Article  Google Scholar 

  21. Pedro, G.T., Verdejo, J.D., Gabriel, M.F., Enrique, V.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28, 18–28 (2009). doi:10.1016/j.cose.2008.08.003

    Article  Google Scholar 

  22. Tarek, S.S.: Wired and wireless intrusion detection system: classifications, good characteristics and state-of-the-art. Comput. Stand. Interfaces 28, 670–694 (2006). doi:10.1016/j.csi.2005.07.002

    Article  Google Scholar 

  23. Wenke, L., Salvatore, J.S., Kui, W.M.: Adaptive intrusion detection: a data mining approach. Artif. Intell. Rev. 14, 533–567 (2000). doi:10.1023/A:1006624031083

    Article  MATH  Google Scholar 

  24. Lee, W., Nimbalkar, R.A., Yee, K.K., Patil, S.B., Desai, P.H., Tran, T.T., Stolfo, S.J.: A data mining and CIDF based approach for detecting novel and distributed intrusions. In: Debar, H., Mé, L., Wu, S.F. (eds.) RAID 2000. LNCS, vol. 1907, pp. 49–65. Springer, Heidelberg (2000). doi:10.1007/3-540-39945-3_4

    Chapter  Google Scholar 

  25. Valdes, A., Skinner, K.: Adaptive, model-based monitoring for cyber attack detection. In: Debar, H., Mé, L., Wu, S.Felix (eds.) RAID 2000. LNCS, vol. 1907, pp. 80–93. Springer, Heidelberg (2000). doi:10.1007/3-540-39945-3_6

    Chapter  Google Scholar 

  26. Nong, Y., Mingming, X., Syed, M.E.: Probabilistic networks with undirected links for anomaly detection. In: Proceedings of the IEEE Systems, Man, and Cybernetics Information Assurance and Security Workshop, West Point, NY, pp. 175–179 (2000)

    Google Scholar 

  27. Gun, K., Nur, Z.H., Malcolm, I.H.: A hierarchical SOM-based intrusion detection system. Eng. Appl. Artif. Intell. 20, 439–451 (2007). doi:10.1016/j.engappai.2006.09.005

    Article  Google Scholar 

  28. Mei-Ling, S., Shu-Ching, C., Kanoksri, S., LiWu, C.: A novel anomaly detection scheme based on principal component classifier. In: Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, Melbourne, FL, USA, pp. 172–179 (2003)

    Google Scholar 

  29. Stuart, S., James, A.H., Joseph, M.M.: Practical automated detection of stealthy portscans. J. Comput. Secur. 10, 105–136 (2002). doi:10.3233/JCS-2002-101-205

    Article  Google Scholar 

  30. Nong, Y., Syed, M.E., Qiang, C., Sean, V.: Multivariate statistical analysis of audit trails for host-based intrusion detection. IEEE Trans. Comput. 51, 810–820 (2002). doi:10.1109/TC.2002.1017701

    Article  Google Scholar 

  31. Alan, B., Chandrika, P., Rasheda, S., Boleslaw, S., Mark, E.: Network-based intrusion detection using neural networks. Intell. Eng. Syst. Through Artif. Neural Netw. 12, 579–584 (2002)

    Google Scholar 

  32. Kathleen, A.J., David, H.D., Cathy, A.S.: An expert system application for network intrusion detection. In: National Computer Security Conference, Washington, DC (United States), 1–4 October (1991)

    Google Scholar 

  33. Wenke, L., Savatore, J.S., Kui, W.M.: Data mining in work flow environments- experiments in intrusion detection. In: Proceedings of the 1999 Conference on Knowledge Discovery and Data Mining (KDD 1999) (1999)

    Google Scholar 

  34. John, L.Z., Ali, G.: Network intrusion detection using an improved competitive learning neural network. In: Proceedings of Second Annual Conference on Communication Networks and Services Research, pp. 190–197. IEEE-Computer Society (2004). doi:10.1109/DNSR.2004.1344728

  35. Mehdi, M., Mohammad, Z.: A neural network based system for intrusion detection and classification of attacks. In: Proceedings of the 2004 IEEE International Conference on Advances in Intelligent Systems-Theory and Applications. Luxembourg-Kirchberg, Luxembourg. IEEE Press, 15–18 November 2004

    Google Scholar 

  36. Susan, C.L., David, V.H.: Training a neural network based intrusion detector to recognize novel attacks. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 31, 294–299 (2001). doi:10.1109/3468.935046

    Article  Google Scholar 

  37. Weiming, H., Wei, H., Steve, M.: Adaboost-based algorithm for network intrusion detection. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 38, 577–583 (2008). doi:10.1109/TSMCB.2007.914695

    Article  Google Scholar 

  38. Bezalel, P., Peter, S.: Introduction to the Theory of Cooperative Games, vol. 34. Springer Science and Business Media, Heidelberg (2007)

    MATH  Google Scholar 

  39. Eric, D.: Non-cooperative games. Ann. Math. 54, 286–295 (2014). doi:10.2307/1969529

    MathSciNet  Google Scholar 

  40. Robert, A.H., Erik, D.D.: Games, Puzzles and Computation. AK Peters, Limited, Natick (2009)

    MATH  Google Scholar 

  41. Walid, S., Zhu, H., Mérouane, D., Are, H., Tamer, B.: Coalitional game theory for communication networks. IEEE Sig. Process. Mag. 26, 77–97 (2009). doi:10.1109/MSP.2009.000000

    Article  Google Scholar 

  42. Kai, M., Xinping, G., Bin, Z.: Symmetrical cooperative strategies in wireless networks: a cooperative game approach. In: 29th Chinese Control Conference (CCC), Beijing, China, pp. 4175–4179. IEEE, 29–31 July 2010

    Google Scholar 

  43. Tanya, R., Shridhar, M.M., Ali, G.: Robust estimation and detection in ad hoc and sensor networks. In: IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS), Vancouver, BC, Canada, pp. 236–245. IEEE, 9–12 October 2006. doi:10.1109/MOBHOC.2006.278562

  44. Vanbien, L., Zhiyong, F., Ping, Z., Yi, H., Xiaomeng, W.: A dynamic spectrum allocation scheme with interference mitigation in cooperative networks. In: Wireless Communications and Networking Conference, WCNC 2008, pp. 3175–3180. IEEE (2008). doi:10.1109/WCNC.2008.554

  45. Hadi, O., Noman, M., Lingyu, W., Mourad, D., Prabir, B.: A game-theoretic intrusion detection model for mobile ad hoc networks. Comput. Commun. 31, 708–721 (2008). doi:10.1016/j.comcom.2007.10.024

    Article  Google Scholar 

  46. Haksub, K., Hyungkeuk, L., Sanghoon, L.: A cross-layer optimization for energy-efficient MAC protocol with delay and rate constraints. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, pp. 2336–2339. IEEE, 22–27 May 2011. doi:10.1109/ICASSP.2011.5946951

  47. Kodialam, M., Lakshman, T. V.: Detecting network intrusions via sampling: a game theoretic approach. In: INFOCOM 2003 Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, San Francisco, California, USA, vol. 3, pp. 1880–1889, March 2003. doi:10.1109/INFCOM.2003.1209210

  48. Shamik, S., Mainak, C., Kevin, K.: A game theoretic framework for power control in wireless sensor networks. IEEE Trans. Comput. 59, 231–242 (2010). doi:10.1109/TC.2009.82

    Article  MathSciNet  Google Scholar 

  49. Animesh, P., Park, J-M.: A game theoretic approach to modeling intrusion detection in mobile ad hoc networks. In: Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, pp. 280–284. IEEE (2004).doi:10.1109/IAW.2004.1437828

  50. He, W., Xia, C., Wang, H., Zhang, C., Ji, Y.: A game theoretical attack-defense model oriented to network security risk assessment. In: International Conference on Computer Science and Software Engineering, vol. 3, pp. 1097–1103. IEEE (2008). doi:10.1109/CSSE.2008.1062

  51. Tansu, A., Tamer, B.: A game theoretic analysis of intrusion detection in access control systems. In: 43rd IEEE Conference on Decision and Control, vol. 2, pp. 1568–1573. IEEE (2004). doi:10.1109/CDC.2004.1430267

  52. Sintayehu, D., Kyle, G., Ladan, G., Reza, G., Srikanta, K.: Reliable data fusion in wireless sensor networks: a dynamic bayesian game approach. In: Proceedings of 2009 IEEE Military Communications Conference, Boston, MA, USA, 18–21 October. IEEE Press (2009). doi:10.1109/MILCOM.2009.5379987

  53. Afrand, A., Sajal, K.D.: Preventing DoS attacks in wireless sensor networks: a repeated game theory approach. Int. J. Netw. Secur. 5, 145–153 (2007)

    Google Scholar 

  54. Charles, P., Zhu, H., Liu, K.J.R.: Cooperation enforcement and learning for optimizing packet forwarding in autonomous wireless networks. IEEE Trans. Wirel. Commun. 7, 3150–3163 (2008). doi:10.1109/TWC.2008.070213

    Article  Google Scholar 

  55. Zhang, X., Cai, Y., Zhang, H.: A game-theoretic dynamic power management policy on wireless sensor network. In: International Conference on Communication Technology, ICCT 2006, Guilin, China, pp. 1–4. IEEE (2006).doi:10.1109/ICCT.2006.341932

Download references

Acknowledgement

The first author would also like to thank the Ministry of Human Resource and Development (MHRD), Government of India for funding her M.Tech program.

Author information

Authors and Affiliations

  1. Computer Science and Engineering, NIT-Hamirpur, Himachal Pradesh, India

    Pallavi Kaushik & Kamlesh Dutta

Authors
  1. Pallavi Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamlesh Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pallavi Kaushik .

Editor information

Editors and Affiliations

  1. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  2. IIT Roorkee, Roorkee, India

    Balasubramanian Raman

  3. CTIEMT, Jalandhar, Punjab, India

    Ashish Kumar Luhach

  4. Department of Mathematics and Computing Science, Saint Mary’s University, Halifax, Nova Scotia, Canada

    Pawan Lingras

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Kaushik, P., Dutta, K. (2017). A Neural Network Model for Intrusion Detection Using a Game Theoretic Approach. In: Singh, D., Raman, B., Luhach, A., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2017. Communications in Computer and Information Science, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-5780-9_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-5780-9_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5779-3

  • Online ISBN: 978-981-10-5780-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation