The precocious classification framework for network intrusion detection system

  • Authors

    • D. Selvamani Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu
    • V. Selvi Department of Computer Science, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu
    2019-04-21
    https://doi.org/10.14419/ijet.v7i4.22844
  • Intrusion Detection System, KDD CUP 99, Feature Selection, Information Gain, Artificial Neural Network

  • The Intrusion Detection System (IDS) can be used broadly for securing the network. Intrusion detection systems (IDS) are typically positioned laterally through former protecting safety automation, like access control and verification, as a subsequent line of resistance that guards data classifications. This projected framework established on the precocious feature selection, which is consent to lessens the number of features generated in the KDD CUP 99 benchmark dataset. The projected framework customs the Back Propagation Neural Networks to recognize the Denial of Service (DoS), where it is a combined variety of attack in the networks.

     

     

  • References

    1. [1] Shengyi Pan, Thomas Morris and Uttam Adhikari, “Developing a hybrid intrusion detection system using data mining for power systems,†IEEE Transactions on Smart Grid, Vol.6, No.6, pp.3104-3113, 2015. https://doi.org/10.1109/TSG.2015.2409775.

      [2] Syed Ali Raza Shah and Biju Issac, “Performance comparison of intrusion detection systems and application of machine learning to Snort system,†Future Generation Computer Systems, Vol. 80, pp.157-170, 2018. https://doi.org/10.1016/j.future.2017.10.016.

      [3] M. Elbasiony, Reda, et al, “A hybrid network intrusion detection framework based on random forests and weighted k-means,†Ain Shams Engineering Journal, Vol. 4, No. 4, pp.753-762, 2013. https://doi.org/10.1016/j.asej.2013.01.003.

      [4] Iftikhar Ahmad, et al., “Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components,†Neural computing and applications, Vol.24, No.7-8, pp.1671-1682, 2014. https://doi.org/10.1007/s00521-013-1370-6.

      [5] Aburomman, Abdulla Amin and Mamun Bin IbneReaz, “A novel SVM-kNN-PSO ensemble method for intrusion detection system,†AppliedSoftComputing, Vol. 38, pp.360-372, 2016. https://doi.org/10.1016/j.asoc.2015.10.011.

      [6] Thaseen, Ikram Sumaiya and CherukuriAswani Kumar, “Intrusion detection model using fusion of chi-square feature selection and multi class SVM,†Journal of King Saud University-Computer and Information Sciences, Vol. 29, No.4, pp.462-472, 2017. https://doi.org/10.1016/j.jksuci.2015.12.004.

      [7] Li, Longjie, et al., “Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO,†Journal of Sensors, 2018. https://doi.org/10.1155/2018/1578314.

      [8] Jadhav, Swati, Hongmei He, and Karl Jenkins. "Information gain directed genetic algorithm wrapper feature selection for credit rating." Applied Soft Computing 69 (2018): 541-553. https://doi.org/10.1016/j.asoc.2018.04.033.

      [9] Venkataraman, Sivakumar, and Rajalakshmi Selvaraj. "Optimal and Novel Hybrid Feature Selection Framework for Effective Data Classification." Advances in Systems, Control and Automation. Springer, Singapore, 2018. 499-514. https://doi.org/10.1007/978-981-10-4762-6_48.

      [10] Ramírez-Gallego, Sergio, et al. "An information theory-based feature selection framework for big data under apache spark." IEEE Transactions on Systems, Man, and Cybernetics: Systems 48.9 (2018): 1441-1453. https://doi.org/10.1109/TSMC.2017.2670926.

      [11] MR Gauthama Raman, et al., “A hypergraph and arithmetic residue-based probabilistic neural network for classification in intrusion detection systems,†NeuralNetworks, Vol. 92, pp.89-97, 2017. https://doi.org/10.1016/j.neunet.2017.01.012.

      [12] Abien Fred M. Agarap, “A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data,†Proceedings of the 2018 10th International Conference on Machine Learning and Computing, ACM, 2018.

      [13] Dataset Source: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

      [14] Lakshmanaprabu SK, K. Shankar, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues, Plácido R. Pinheiro, Victor Hugo C. de Albuquerque. “Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clusteringâ€. Complexity, 2018: 1-9. https://doi.org/10.1155/2018/3569351.

      [15] Eka Sugiyarti, Kamarul Azmi Jasmi, Bushrah Basiron, Miftachul Huda, K. Shankar, Andino Maseleno, “Decision Support System of Scholarship Grantee Selection using Data Miningâ€, International Journal of Pure and Applied Mathematics, 119.15 (2018): 2239-2249.

      [16] Lakshmanaprabu SK, K. Shankar, Ashish Khanna, Deepak Gupta, Joel J. P. C. Rodrigues, Plácido R. Pinheiro, Victor Hugo C. de Albuquerque. Effective Features to Classify Big Data using Social Internet of Things. IEEE Access, 6 (2018): 24196-24204. https://doi.org/10.1109/ACCESS.2018.2830651.

      [17] Andino Maseleno, Alicia Y.C. Tang, Moamin A. Mahmoud, Marini Othman, Suntiaji Yudo Negoro, Soukaina Boukri, K. Shankar, Satria Abadi, Muhamad Muslihudin. “The Application of Decision Support System by Using Fuzzy Saw Method in Determining the Feasibility of Electrical Installations in Customer’s Houseâ€. International Journal of Pure and Applied Mathematics, 119. 16 (2018): 4277-4286.

      [18] Mohamed Elhoseny, K. Shankar, S. K. Lakshmanaprabu, Andino Maseleno, N. Arunkumar. Hybrid optimization with cryptography encryption for medical image security in Internet of Things. Neural Computing and Applications. 2018. https://doi.org/10.1007/s00521-018-3801-x.

      [19] K. Shankar, Mohamed Elhoseny, E. Dhiravida chelvi, SK. Lakshmanaprabu, Wanqing Wu. An Efficient Optimal Key Based Chaos Function for Medical Image Security. IEEE Access. 2018. https://doi.org/10.1109/ACCESS.2018.2874026.

      [20] T. Avudaiappan, R. Balasubramanian, S. Sundara Pandiyan, M. Saravanan, S. K. Lakshmanaprabu, K. Shankar, “Medical Image Security Using Dual Encryption with Oppositional Based Optimization Algorithm, Journal of Medical Systemsâ€, 42.11 (2018) 1-11. https://doi.org/10.1007/s10916-018-1053-z.

      [21] K.Shankar and P.Eswaran. “RGB Based Multiple Share Creation in Visual Cryptography with Aid of Elliptic Curve Cryptographyâ€, China Communications, 14. 2 (2017): 118-130. https://doi.org/10.1109/CC.2017.7868160.

      [22] LakshmanaprabuS.K, Sachi Nandan Mohanty, K. Shankar, Arunkumar N, GustavoRamireze. “Optimal deep learning model for classification of lung cancer on CT imagesâ€, Future Generation Computer Systems. 2018. https://doi.org/10.1016/j.future.2018.10.009.

      K. Shankar, Lakshmanaprabu S.K, Deepak Gupta, Andino Maseleno, Victor Hugo C. de Albuquerque. Optimal Features Based Multi Kernel SVM Approach for Thyroid Disease Classification. The Journal of Supercomputing, 2018. https://doi.org/10.1007/s11227-018-2469-4 https://doi.org/10.1007/s11227-018-2469-4

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  • How to Cite

    Selvamani, D., & Selvi, V. (2019). The precocious classification framework for network intrusion detection system. International Journal of Engineering & Technology, 7(4), 5934-5939. https://doi.org/10.14419/ijet.v7i4.22844