Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions

  • Authors

    • Ravinder Korani
    • Dr P. Chandra Sekhar Reddy
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10982
  • IOT, IDS, Intrusion, Intrusion Scope Heuristic, Benign Scope Heuristic, open deployment

  • Abstract

    Conventional intrusion detection mechanisms face serious limitations in identifying heterogeneous and distributed type of intrusions over the IoT environment. This is due to inadequate resources and open deployment environment of IoT. Accordingly, ensuring data security and privacy are tough challenges in the practical context. This manuscript discusses various aspects of networking security and related challenges along with the concepts of system architecture. Further, endeavored to define a machine learning model that outlines two heuristics called Intrusion Scope Heuristic ( ), and benign scope heuristic ( ), which further uses in predictive analysis to identify the IOT network transaction is prone to intrusion or benign. The experimental study revealed the significance of the proposal with maximal detection accuracy, and minimal miss rate.

     

  • References

    1. [1] Kun, Z.,Meng, X.: (2009) Research and prevention measures of computer network security. Fujian 10, 102–103.

      [2] United States General Accounting Office: (1996) Computer Attacks at Department of Defense Pose Increasing Risks. GAO/AIMD-96-84 Defense Information Security, Washington DC.

      [3] United States General Accounting Office: (1996) Opportunities for improved OMB over sight of agency practices. GAO/AIMD Information Security, Washington DC.

      [4] Conti, J.P.:(2006) The Internet of things. Commun. Eng. 4(6), 20–25.

      [5] Zhou, J.: (2009) Wireless sensor network intrusion detection model research. In: CIE 16th Information Theory Academic Conference Proceedings, pp. 799–804. Electronic Industry Press, Beijing.

      [6] Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on software engineering, (2), 222-232.

      [7] Ariu, D., Tronci, R., & Giacinto, G. (2011). HMMPayl: An intrusion detection system based on Hidden Markov Models. computers & security, 30(4), 221-241.

      [8] Koc, L., Mazzuchi, T. A., & Sarkani, S. (2012). A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier. Expert Systems with Applications, 39(18), 13492-13500.

      [9] Lin, W. C., Ke, S. W., & Tsai, C. F. (2015). CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-based systems, 78, 13-21.

      [10] Weller-Fahy, D. J., Borghetti, B. J., & Sodemann, A. A. (2015). A survey of distance and similarity measures used within network intrusion anomaly detection. IEEE Communications Surveys & Tutorials, 17(1), 70-91.

      [11] Thottan, M., & Ji, C. (2003). Anomaly detection in IP networks. IEEE Transactions on signal processing, 51(8), 2191-2204.

      [12] Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2014). Network anomaly detection: methods, systems and tools. IEEE communications surveys & tutorials, 16(1), 303-336.

      [13] Theiler, J. P., & Cai, D. M. (2003, September). Resampling approach for anomaly detection in multispectral images. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX (Vol. 5093, pp. 230-241). International Society for Optics and Photonics.

      [14] Chandola, V., Banerjee, A., & Kumar, V. (2012). Anomaly detection for discrete sequences: A survey. IEEE Transactions on Knowledge and Data Engineering, 24(5), 823-839.

      [15] Casas, P., Mazel, J., & Owezarski, P. (2012). Unsupervised network intrusion detection systems: Detecting the unknown without knowledge. Computer Communications, 35(7), 772-783.

      [16] De la Hoz, E., De La Hoz, E., Ortiz, A., Ortega, J., & Prieto, B. (2015). PCA filtering and probabilistic SOM for network intrusion detection. Neurocomputing, 164, 71-81.

      [17] Bostani, H., & Sheikhan, M. (2017). Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept. Pattern Recognition, 62, 56-72.

      [18] Guo, C., Ping, Y., Liu, N., & Luo, S. S. (2016). A two-level hybrid approach for intrusion detection. Neurocomputing, 214, 391-400.

      [19] Horng, S. J., Su, M. Y., Chen, Y. H., Kao, T. W., Chen, R. J., Lai, J. L., & Perkasa, C. D. (2011). A novel intrusion detection system based on hierarchical clustering and support vector machines. Expert systems with Applications, 38(1), 306-313.

      [20] Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2014). A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE transactions on parallel and distributed systems, 25(2), 447-456.

      [21] Pajouh, H. H., Dastghaibyfard, G., & Hashemi, S. (2017). Two-tier network anomaly detection model: a machine learning approach. Journal of Intelligent Information Systems, 48(1), 61-74.

      [22] Osanaiye, O., Choo, K. K. R., & Dlodlo, M. (2016). Distributed denial of service (DDoS) resilience in cloud: review and conceptual cloud DDoS mitigation framework. Journal of Network and Computer Applications, 67, 147-165.

      [23] Iqbal, S., Kiah, M. L. M., Dhaghighi, B., Hussain, M., Khan, S., Khan, M. K., & Choo, K. K. R. (2016). On cloud security attacks: A taxonomy and intrusion detection and prevention as a service. Journal of Network and Computer Applications, 74, 98-120.

      [24] Ambusaidi, M. A., He, X., Nanda, P., & Tan, Z. (2016). Building an intrusion detection system using a filter-based feature selection algorithm. IEEE transactions on computers, 65(10), 2986-2998.

      [25] Daryabar, F., Dehghantanha, A., Udzir, N. I., & bin Shamsuddin, S. (2012, June). Towards secure model for SCADA systems. In Cyber Security, Cyber Warfare and Digital Forensic (CyberSec), 2012 International Conference on (pp. 60-64). IEEE.

      [26] Pan, S., Morris, T., & Adhikari, U. (2015). Developing a hybrid intrusion detection system using data mining for power systems. IEEE Transactions on Smart Grid, 6(6), 3104-3113.

      [27] Zhou, C., Huang, S., Xiong, N., Yang, S. H., Li, H., Qin, Y., & Li, X. (2015). Design and analysis of multimodel-based anomaly intrusion detection systems in industrial process automation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(10), 1345-1360.

      [28] Ashraf, Q. M., & Habaebi, M. H. (2015). Autonomic schemes for threat mitigation in Internet of Things. Journal of Network and Computer Applications, 49, 112-127.

      [29] Ning, H., Liu, H., & Yang, L. T. (2015). Aggregated-proof based hierarchical authentication scheme for the internet of things. IEEE Transactions on Parallel and Distributed Systems, 26(3), 657-667.

      [30] Cao, X., Shila, D. M., Cheng, Y., Yang, Z., Zhou, Y., & Chen, J. (2016). Ghost-in-ZigBee: Energy depletion attack on ZigBee-Based wireless networks. IEEE Internet of Things Journal, 3(5), 816-829.

      [31] Chen, Q., Abdelwahed, S., & Erradi, A. (2014). A model-based validated autonomic approach to self-protect computing systems. IEEE Internet of Things Journal, 1(5), 446-460.

      [32] Teixeira, F. A., Machado, G. V., Fonseca, P. M., Pereira, F. M. Q., Wong, H. C., Nogueira, J. M. S., & Oliveira, L. B. (2015). Defending Internet of Things against Exploits. IEEE Latin America Transactions, 13(4), 1112-1119.

      [33] Jović, A., Brkić, K., & Bogunović, N. (2015, May). A review of feature selection methods with applications. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2015 38th International Convention on (pp. 1200-1205). IEEE.

      [34] Miettinen, M., Marchal, S., Hafeez, I., Asokan, N., Sadeghi, A. R., & Tarkoma, S. (2017, June). IoT Sentinel: Automated device-type identification for security enforcement in IoT. In Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on (pp. 2177-2184). IEEE.

      [35] Somol, P., Pudil, P., NovoviÄová, J., & Paclık, P. (1999). Adaptive floating search methods in feature selection. Pattern recognition letters, 20(11-13), 1157-1163.

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  • How to Cite

    Korani, R., & P. Chandra Sekhar Reddy, D. (2018). Anomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions. International Journal of Engineering & Technology, 7(2.7), 797-802. https://doi.org/10.14419/ijet.v7i2.7.10982

    Received date: 2018-04-02

    Accepted date: 2018-04-02

    Published date: 2018-03-18