Supervised AFRC (Ada boost fast regression) machine learning algorithm for enhancing performance of intrusion detection system

  • Authors

    • Abhishek Jain Amity University Haryana University
    • Dr. Khushboo Tripathi Amity University Haryana
    2019-04-07
    https://doi.org/10.14419/ijet.v7i4.23801
  • Adaboost Fast Regression Classifier (AFRC), Classifier, CICIDS2017, Malicious Activity, Security.

  • Abstract

    In recent wireless network play critical role in every activity of human life. This wireless network process sensitive data network communication requires appropriate cyber security. In order to offer cyber security in computer network antivirus, user authentication schemes, firewalls and access control techniques has been developed to detect abnormal activities and potential attacks in computer network. To ensure security Intrusion Detection System (IDS) is designed for network security. In this paper proposed a Adaboost Fast Regression Classifier for attack or malicious activity detection in IDS system. For analysis in this research used CICIDS 2017 dataset the main advantage of this dataset is redundant data are minimal hence accuracy of malicious detection is increased. Collected dataset is fed into MATLAB and evaluated with proposed AFRC mechanism. In proposed AFRC scheme AdaBoost classifier and regression classifier are combined for attack identification and classification. Comparative analysis of proposed AFRC scheme with existing approach exhibits significant performance in terms of attack identification with reduced computational cost.

     

     

     

  • References

    1. [1] Portugal, I., Alencar, P., & Cowan, D. (2017). The use of machine learning algorithms in recommender systems: a systematic review. Expert Systems with Applications.

      [2] Belavagi, M. C., & Muniyal, B. (2016). Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Computer Science, 89, 117-123. https://doi.org/10.1016/j.procs.2016.06.016.

      [3] Richert, W. (2013). Building machine learning systems with Python. Packt Publishing Ltd.

      [4] Yu, Z., & Tsai, J. J. (2008, June). A framework of machine learning based intrusion detection for wireless sensor networks. In Sensor Networks, Ubiquitous and Trustworthy Computing, 2008. SUTC'08. IEEE International Conference on (pp. 272-279). IEEE. https://doi.org/10.1109/SUTC.2008.39.

      [5] Belavagi, M. C., & Muniyal, B. (2016, August). Game theoretic approach towards intrusion detection. In Inventive Computation Technologies (ICICT), International Conference on (Vol. 1, pp. 1-5). IEEE.

      [6] Altwaijry, H., & Algarny, S. (2012). Bayesian based intrusion detection system. Journal of King Saud University-Computer and Information Sciences, 24(1), 1-6. https://doi.org/10.1016/j.jksuci.2011.10.001.

      [7] Panda, M., & Patra, M. R. (2009, December). Semi-Naïve Bayesian method for network intrusion detection system. In International Conference on Neural Information Processing (pp. 614-621). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_70.

      [8] Raman, M. G., Somu, N., Kirthivasan, K., Liscano, R., & Sriram, V. S. (2017). An efficient intrusion detection system based on Hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine. Knowledge-Based Systems, 134, 1-12. https://doi.org/10.1016/j.knosys.2017.07.005.

      [9] Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. Expert Systems with Applications, 36(10), 11994-12000. https://doi.org/10.1016/j.eswa.2009.05.029.

      [10] Chung, Y. Y., & Wahid, N. (2012). A hybrid network intrusion detection system using simplified swarm optimization (SSO). Applied Soft Computing, 12(9), 3014-3022. https://doi.org/10.1016/j.asoc.2012.04.020.

      [11] Onyeji, I., Bazilian, M., & Bronk, C. (2014). Cyber security and critical energy infrastructure. The Electricity Journal, 27(2), 52-60 https://doi.org/10.1016/j.tej.2014.01.011.

      [12] Traynor, I. Russia accused of unleashing cyberwar to disable Estonia. May 17, 2007

      [13] Danchev, D. Georgia President’s web site under DDoS Attack from Russian hackers.†zdnet. com, 22 July 2008. U http://blogs. zdnet. com/security

      [14] Scarfone, K., & Mell, P. (2007). Guide to intrusion detection and prevention systems (idps). NIST special publication, 800(2007), 94. https://doi.org/10.6028/NIST.SP.800-94.

      [15] Kuang, F., Xu, W., & Zhang, S. (2014). A novel hybrid KPCA and SVM with GA model for intrusion detection. Applied Soft Computing, 18, 178-184 https://doi.org/10.1016/j.asoc.2014.01.028.

      [16] Huang, J., Zhu, Q., Yang, L., Cheng, D., & Wu, Q. (2017). A novel outlier cluster detection algorithm without top-n parameter. Knowledge-Based Systems, 121, 32-40. https://doi.org/10.1016/j.knosys.2017.01.013.

      [17] Raman, M. G., Somu, N., Kirthivasan, K., & Sriram, V. S. (2017). A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems. Neural Networks, 92, 89-97 https://doi.org/10.1016/j.neunet.2017.01.012.

      [18] Yang, L., & Shen, Q. (2011). Adaptive fuzzy interpolation. IEEE Transactions on Fuzzy Systems, 19(6), 1107-1126 https://doi.org/10.1109/TFUZZ.2011.2161584.

      [19] Lin, S. W., Ying, K. C., Chen, S. C., & Lee, Z. J. (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert systems with applications, 35(4), 1817-1824. https://doi.org/10.1016/j.eswa.2007.08.088.

      [20] 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. https://doi.org/10.1109/TC.2016.2519914.

      [21] Tang, J., Alelyani, S., & Liu, H. (2014). Feature selection for classification: A review. Data Classification: Algorithms and Applications, 37.

      [22] Goswami, S., & Chakrabarti, A. (2014). Feature selection: A practitioner view. International Journal of Information Technology and Computer Science (IJITCS), 6(11), 66 https://doi.org/10.5815/ijitcs.2014.11.10.

      [23] 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 https://doi.org/10.1109/TPDS.2013.146.

      [24] Inayat, Z., Gani, A., Anuar, N. B., Khan, M. K., & Anwar, S. (2016). Intrusion response systems: Foundations, design, and challenges. Journal of Network and Computer Applications, 62, 53-74 https://doi.org/10.1016/j.jnca.2015.12.006.

      [25] Hubballi, N., & Suryanarayanan, V. (2014). False alarm minimization techniques in signature-based intrusion detection systems: A survey. Computer Communications, 49, 1-17. https://doi.org/10.1016/j.comcom.2014.04.012.

      [26] Ni, X., He, D., Chan, S., & Ahmad, F. (2016, June). Network anomaly detection using unsupervised feature selection and density peak clustering. In International Conference on Applied Cryptography and Network Security (pp. 212-227). Springer, Cham. https://doi.org/10.1007/978-3-319-39555-5_12.

      [27] Villalba, L. J. G., Orozco, A. L. S., & Vidal, J. M. (2015). Anomaly-Based Network Intrusion Detection System. IEEE Latin America Transactions, 13(3), 850-855.

  • Downloads

  • How to Cite

    Jain, A., & Khushboo Tripathi, D. (2019). Supervised AFRC (Ada boost fast regression) machine learning algorithm for enhancing performance of intrusion detection system. International Journal of Engineering & Technology, 7(4), 5622-5628. https://doi.org/10.14419/ijet.v7i4.23801

    Received date: 2018-12-12

    Accepted date: 2019-03-29

    Published date: 2019-04-07