Cluster Computing

, Volume 22, Supplement 5, pp 12429–12441 | Cite as

An intelligent intrusion detection system for secure wireless communication using IPSO and negative selection classifier

  • G. BhuvaneswariEmail author
  • G. Manikandan


Internet security is very crucial need in this real world environment due to the rise of e-business, e-learning, and e-governance. Intellectual data mining applications are useful for producing security while accessing through the internet from cloud databases. Currently, the cloud security researchers are not in a position to introduce more reliable, secure and effective real-time intrusion detection systems for detecting the intruders in online. For fulfilling this requirement, we propose a new intelligent classification model for anomaly detection which detects the intruders effectively in cloud networks using a combination of an enhanced incremental particle swarm optimization and negative selection algorithm. Moreover, we enhanced these two methods by the uses of Minkowski distance metric for effective decision making. The experimental results of the proposed classification model show that this system detects anomalies with low false alarm rate and high detection rate when tested with NSL-KDD dataset which is modified from KDD 1999 Cup dataset.


Internet security Intrusion detection system Particle swarm optimization Negative selection Clustering 


  1. 1.
    Li, D., Liu, S., Zhang, H.: A negative selection algorithm with online adaptive learning under small samples for anomaly detection. Neurocomputing 149(Part–B), 515–525 (2015)CrossRefGoogle Scholar
  2. 2.
    Ganapathy, S., Kulothungan, K., Muthurajkumar, S., Vijayalakshmi, M., Yogesh, P., Kannan, A.: Intelligent feature selection and classification techniques for intrusion detection in networks: a survey. EURASIP J. Wirel. Commun. Netw. 271, 1–16 (2013)Google Scholar
  3. 3.
    Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: models and applications. Appl. Soft Comput. 11(2), 1574–1587 (2011)CrossRefGoogle Scholar
  4. 4.
    Freitas, A.A., Timmis, J.: Revisiting the foundations of artificial immune systems for data mining. IEEE Trans. Evol. Comput. 11(4), 521–540 (2007)CrossRefGoogle Scholar
  5. 5.
    González, F.A., Dasgupta, D.: Anomaly detection using real-valued negative selection. Genet. Progr. Evol. 4(4), 383–403 (2003)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Li, Y., Zhang, Y. et al.: Class conditional distance metric for 3D protein structure classification. In: Proceeding of the 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, pp. 1–4 (2011)Google Scholar
  7. 7.
    Forrest, S., Perelson, A.S., Allen, L. et al.: Self-nonself Discrimination in a Computer. In: Proceeding of the IEEE Symposium on Research in Security and Privacy, Oakland, pp. 202–212 (1994)Google Scholar
  8. 8.
    Bereta, M., Burczyński, T.: Immune K-means and negative selection algorithms for data analysis. Inf. Sci. 179(10), 1407–1425 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhou, J., Dasgupta, D.: Revisiting negative selection algorithms. Evol. Comput. 15(2), 223–251 (2007)CrossRefGoogle Scholar
  10. 10.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)CrossRefGoogle Scholar
  11. 11.
    Ganapathy, S., Kulothungan, K., Yogesh, P., Kannan, A.: A novel weighted fuzzy C-means clustering based on immune genetic algorithm for intrusion detection. Procedia Eng. 38, 1750–1757 (2012)CrossRefGoogle Scholar
  12. 12.
    Shamshirband, S., Anuar, N.B., Kiah, M.L.M., Rohani, V.A., Petković, D., Misra, S., Khan, A.N.: J. Netw. Comput. Appl. Co-FAIS: cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks 42, 102–117 (2014)Google Scholar
  13. 13.
    Zhou, J., Dasgupta, D.: Real-valued negative selection algorithm with variable-sized detectors. In: Proceeding of Genetic and Evolutionary Computation Conference, Washington, pp. 287–298 (2004)Google Scholar
  14. 14.
    Dasgupta, D., González, F.: An immunity-based technique to characterize intrusions in computer networks. IEEE Trans. Evol. Comput. 6(3), 281–291 (2002)CrossRefGoogle Scholar
  15. 15.
    Shapiro, J.M., Lamont, G.B., Peterson, G.L.: An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection. In: Proceeding of the 2005 Workshops on Genetic and Evolutionary Computation, Washington, pp. 337–344 (2005)Google Scholar
  16. 16.
    Balachandran, S., Dasgupta, D., Nino, F. et al.: A framework for evolving multi-shaped detectors in negative selection. In: Proceeding of the IEEE Symposium on Computational Intelligence, Hawaii, pp. 401–408 (2007)Google Scholar
  17. 17.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufman Publishers, Burlington (2001)Google Scholar
  18. 18.
    Eberhart, R.C., Simpson, P., Dobbins, R.: 1996 Computational Intelligence PC Tools. Academic Press, Boston (1996)Google Scholar
  19. 19.
    Tsai, C.-W.: Incremental particle swarm optimisation for intrusion detection. IET Netw. 2(3), 124–130 (2013)CrossRefGoogle Scholar
  20. 20.
    Ghanem, T.F., Elkilani, W.S., Abdul-kader, H.M.: A hybrid approach for efficient anomaly detection using metaheuristic methods. J. Adv. Res. 6(4), 609–619 (2015)CrossRefGoogle Scholar
  21. 21.
    de Amorim, R.C..: Constrained clustering with minkowski weighted K-means. In: 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics, pp. 13–17 (2012)Google Scholar
  22. 22.
    Karami, A., Guerrero-Zapata, M.: A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks. Neurocomputing 149(Part–C), 1253–1269 (2015)CrossRefGoogle Scholar
  23. 23.
    Elhag, S., Fernandez, A., Bawakid, A., Alshomrani, S., Herrera, F.: On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst. Appl. 42, 193–202 (2015)CrossRefGoogle Scholar
  24. 24.
    Ganapathy, S., Sethukkarasi, R., Yogesh, P., Vijayakumar, P., Kannan, A.: An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization. Sadhana 39(2), 283–302 (2014)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Aziz, A.S.A., Salama, M., Ella Hassanien, A., El-Ola Hanafi, S.: Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system. In: FedCSIS Proceedings of Federated Conference on Computer Science and Information Systems, Wroclaw, IEEE, pp. 597–602 (2012)Google Scholar
  26. 26.
    Ganapathy, S., Yogesh, P., Kannan, A.: Intelligent agent based intrusion detection system using enhanced multiclass SVM. Comput. Intell. Neurosci. 2012, 1–10 (2012)CrossRefGoogle Scholar
  27. 27.
    Cho, J.-H., Chen, I.-R.: Model-based evaluation of distributed intrusion detection protocols for mobile group communication systems. Wirel. Pers. Commun. 60(4), 725–750 (2011)CrossRefGoogle Scholar
  28. 28.
    Selvi, M., Velvizhy, P., Ganapathy, S., Khanna Nehemiah, H., Kannan, A.: A rule based delay constrained energy efficient routing technique for wireless sensor networks. Clust. Comput. (2017).
  29. 29.
    Logambigai, R., Arputharaj, K.: Fuzzy logic based unequal clustering for wireless sensor networks. Wirel. Netw. 22, 945–957 (2016)CrossRefGoogle Scholar
  30. 30.
    Muthurajkumar, S., Ganapathy, S., Vijayalakshmi, M., Kannan, A.: An intelligent secured and energy efficient routing algorithm for MANETs. Wirel. Pers. Commun. 96(2), 1753–1769 (2017)CrossRefGoogle Scholar
  31. 31.
    Sannasi, G., Vijayakumar, P., Yogesh, P., Kannan, A.: An intelligent CRF based feature selection for effective intrusion detection. Int. Arab J. Inf. Technol. (IAJIT) 13(1), 1–16 (2016)Google Scholar
  32. 32.
    Rajeswari, A.R., Kulothungan, K., Ganapathy, S., Kannan, A.: Malicious nodes detection in MANET using back-off clustering approach. Circuits Syst. 7(8), 2070–2077 (2016)CrossRefGoogle Scholar
  33. 33.
    Varatharajan, R., Manogaran, G., Priyan, M.K., Sundarasekar, R.: Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust. Comput. (2017).
  34. 34.
    IsmailaIdris, Ali, S.: Improved email spam detection model with negative selection algorithm and particle swarm optimization. Appl. Soft Comput. 22, 11–27 (2014)Google Scholar
  35. 35.
    Gao, X.Z., Ovaska, S.J., Wang, X.: Genetic algorithms based detector generation in negative selection algorithm. In: SMCals/06 Proceedings of IEEE Mountain Workshop on Adaptive and Learning Systems, Utah, Logan, USA, IEEE, pp. 133–137 (2006)Google Scholar
  36. 36.
    Wang, D., Zhang, F., Xi, L.: Evolving boundary detector for anomaly detection. Expert Syst. Appl. 38(3), 2412–2420 (2011)CrossRefGoogle Scholar
  37. 37.
    Chung, Y.Y., Wahid, N.: A hybrid network intrusion detection system using simplified swarm optimization (SSO). Appl. Soft Comput. 12(9), 3014–3022 (2012)CrossRefGoogle Scholar
  38. 38.
    Zhai, S., Jiang, T.: A novel particle swarm optimization trained support vector machine for automatic sense-through-foliage target recognition system. Knowl. Based Syst. 65, 50–59 (2014)CrossRefGoogle Scholar
  39. 39.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)CrossRefGoogle Scholar
  40. 40.
    Bridges, S.M., Vaughn, R.B.: Fuzzy data mining and genetic algorithms applied to intrusion detection. In: Proceedings of the National Information Systems Security Conference, pp. 16–19 (2000)Google Scholar
  41. 41.
    Srinoy, S.: Intrusion detection model based on particle swarm optimization and support vector machine. In: Proceedings of the IEEE Symposium Computational Intelligence in Security and Defense Applications, pp. 186–192 (2007)Google Scholar
  42. 42.
    Ou, C.M., Ou, C.R., Wang, Y.T.: Agent Based Artificial Immune Systems (ABAIS) for Intrusion Detections: Inspiration from Danger Theory. In: Hakansson, A., Hartung, R. (eds.) Agent and Multi-agent Systems in Distributed Systems—Digital Economy and E-Commerce, pp. 67–94. Springer, Berlin (2013)CrossRefGoogle Scholar
  43. 43.
    Kabir, E., Jiankun, H., Wang, H., Zhuo, G.: A novel statistical technique for intrusion detection systems. Future Gener. Comput. Syst. 79(1), 303–318 (2018)CrossRefGoogle Scholar
  44. 44.
    Hamed, T., Dara, R., Kremer, S.C.: Network intrusion detection system based on recursive feature addition and bigram technique. Comput. Secur. 73, 137–155 (2018)CrossRefGoogle Scholar
  45. 45.
    Amin, A., Mamun, A., Reaz, B.I.: A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems. Inf. Sci. 414, 225–246 (2017)CrossRefGoogle Scholar
  46. 46.
    Raman, M.R.G., Somu, N., Kirthivasan, K., Liscano, R., Sriram, V.S.S.: An efficient intrusion detection system based on hypergraph—genetic algorithm for parameter optimization and feature selection in support vector machine. Knowl. Based Syst. 134, 1–12 (2017)CrossRefGoogle Scholar
  47. 47.
    Devi, R., Jha, R.K., Gupta, A., Jain, S., Kumar, P.: Implementation of intrusion detection system using adaptive neuro-fuzzy inference system for 5G wireless communication network. AEU Int. J. Electron. Commun. 74, 94–106 (2017)CrossRefGoogle Scholar
  48. 48.
    Balthrop, J., Forrest, S., Glickman, M.R.: Revisiting LISYS: Parameters and Normal Behavior, In: Proceedings of the 2002 Congress on Evolutionary Computing (2002)Google Scholar
  49. 49.
    Wang, C., Zhao, Y.: A new fault detection method based on artificial immune systems. Asia Pac. J. Chem. Eng. 3(6), 706–711 (2008)CrossRefGoogle Scholar
  50. 50.
    Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: CISDA 2009 Proceedings of IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, Canada, pp. 1–6 (2009)Google Scholar
  51. 51.
    Ugray, Z., Lasdon, L., Plummer, J., Glover, F., Kelly, J., Mart, R.: Scatter search and local NLP solvers: a multi-start framework for global optimization. Inf. J. Comput. 19(3), 328–340 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringDMI College of EngineeringChennaiIndia
  2. 2.Department of Computer Science and EngineeringTirumala Engineering CollegeTelenganaIndia

Personalised recommendations