Abstract
The detection of network attacks on computer systems remains an attractive but challenging research scope. As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. The incorporation of a single parallel hidden layer feed-forward neural network to the Fast Learning Network (FLN) architecture gave rise to the improved Extreme Learning Machine (ELM). The input weights and hidden layer biases are randomly generated. In this paper, the particle swan optimization algorithm (PSO) was used to obtain an optimal set of initial parameters for Reduce Kernel FLN (RK-FLN), thus, creating an optimal RKFLN classifier named PSO-RKELM. The derived model was rigorously compared to four models, including basic ELM, basic FLN, Reduce Kernel ELM (RK-ELM), and RK-FLN. The approach was tested on the KDD Cup99 intrusion detection dataset and the results proved the proposed PSO-RKFLN as an accurate, reliable, and effective classification algorithm.
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References
Buczak, A., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutorials, vol. PP, no. 99, p. 1, 2015
Patel, A., Taghavi, M., Bakhtiyari, K., Celestino Jr J.: An intrusion detection and prevention system in cloud computing: a systematic review. J. Netw. Comput. Appl. 36(1), 25–41 (2013)
Liao, H.-J., Lin, C.-H.R., Lin, Y.-C.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2012)
Liao, H.J., Richard Lin, C.H., Lin, Y.C., Tung, K.Y.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2013)
Tsai, C., Hsu, Y., Lin, C., Lin, W.: Expert systems with applications intrusion detection by machine learning: a review. Expert Syst. Appl. 36(10), 11994–12000 (2009)
Fossaceca, J.M., Mazzuchi, T.A., Sarkani, S.: MARK-ELM: Application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection. Expert Syst. Appl. 42(8), 4062–4080 (2015)
Mishra, P., Pilli, E.S., Varadharajan, V., Tupakula, U.: Intrusion detection techniques in cloud environment: a survey. J. Netw. Comput. Appl. 77, pp. 18–47, October 2016
Jaiganesh, V., Sumathi, P.: Kernelized extreme learning machine with levenberg-marquardt learning approach towards intrusion detection. Int. J. Comput. Appl. 54(14), 38–44 (2012)
Udaya Sampath, X.W., Perera Miriya Thanthrige, K., Samarabandu, J.: Machine learning techniques for intrusion detection. IEEE Can. Conf. Electr. Comput. Eng. 1–10 (2016)
Aslahi-Shahri, B.M., et al.: A hybrid method consisting of GA and SVM for intrusion detection system. Neural Comput. Appl. 27(6), 1669–1676 (2016)
Atefi, K., Yahya, S., Dak, A.Y., Atefi, A.: A Hybrid Intrusion detection system based on differen machine learning algorithms. In: Proceedings of the 4th International Conference Computing Informatics, no. 22, pp. 312–320 (2013)
Ding, S., Xu, X., Nie, R.: Extreme learning machine and its applications. Neural Comput. Appl. 25(3–4), 549–556 (2014)
Singh, R., Kumar, H., Singla, R.K.: An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Syst. Appl. 42(22), 8609–8624 (2015)
Ali, M.H., Zolkipli, M.F., Mohammed, M.A., Jaber, M.M.: Enhance of extreme learning machine-genetic algorithm hybrid based on intrusion detection system. J. Eng. Appl. Sci. 12(16), 4180–4185 (2017)
Lu, H., Du, B., Liu, J., Xia, H., Yeap, W.K.: A kernel extreme learning machine algorithm based on improved particle swam optimization. Memetic Comput. 9(2), 121–128 (2017)
Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. man, Cybern. Part B, Cybern. 42, (2), 513–529 (2012)
Pal, M., Maxwell, A.E., Warner, T.A.: Kernel-based extreme learning machine for remote-sensing image classification. Remote Sens. Lett. 4(9), 853–862 (2013)
Liu, B., Tang, L., Wang, J., Li, A., Hao, Y.: 2-D defect profile reconstruction from ultrasonic guided wave signals based on QGA-kernelized ELM. Neurocomputing 128, 217–223 (2014)
Deng, W.Y., Zheng, Q.H., Wang, Z.M.: Cross-person activity recognition using reduced kernel extreme learning machine. Neural Netw. 53, 1–7 (2014)
Chen, H.L., Wang, G., Ma, C., Cai, Z.N., Bin Liu, W., Wang, S. J.: An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’s disease. Neurocomputing 184, 131–144 (2016)
Chen, C., Li, W., Su, H., Liu, K.: Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sens. 6(6), 5795–5814 (2014)
Fu, H., Vong, C.-M., Wong, P.-K., Yang, Z.: Fast detection of impact location using kernel extreme learning machine. Neural Comput. Appl. 1–10 (2014)
Li, L., Wang, C., Li, W., Chen, J.: Hyperspectral image classification by AdaBoost weighted composite kernel extreme learning machines. Neurocomputing 275, 1725–1733 (2018)
Wang, Y., Wang, A.N., Ai, Q., Sun, H.J.: An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson’s disease. Biomed. Signal Process. Control 38, 400–410 (2017)
Wang, M., et al.: Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267, 69–84 (2017)
Li, X., Niu, P., Li, G.: An adaptive extreme learning machine for modeling NOx emission of a 300 MW circulating fluidized bed boiler (2017)
Li, G., Niu, P.: Combustion optimization of a coal-fired boiler with double linear fast learning network (2014)
Abadeh, M.S., Mohamadi, H., Habibi, J.: Design and analysis of genetic fuzzy systems for intrusion detection in computer networks. Expert Syst. Appl. 38(6), 7067–7075 (2011)
Chen, T., Zhang, X., Jin, S., Kim, O.: Efficient classification using parallel and scalable compressed model and its application on intrusion detection. Expert Syst. Appl. 41(13), 5972–5983 (2014)
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset. Expert Syst. Appl. 38(5), 5947–5957 (2011)
Mitchell, R., Chen, I.-R.: A survey of intrusion detection techniques. Comput. Secur. 12(4), 405–418 (2014)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications CISDA 2009 (June 2009)
Engen, V., Vincent, J., Phalp, K.: Exploring discrepancies in findings obtained with the KDD Cup’99 data set. Intell. Data Anal. 15(2), 251–276 (2011)
Hu, W., Gao, J., Wang, Y., Wu, O., Maybank, S.: Online adaboost-based parameterized methods for dynamic distributed network intrusion detection. IEEE Trans. Cybern. 44(1), 66–82 (2014)
Weller-Fahy, D.J.: Network intrusion dataset assessment, p. 114 (2013)
Chou, T.-S., Fan, J., Fan, S., Makki, K.: Ensemble of machine learning algorithms for intrusion detection. In: 2009 IEEE International Conference System Man and Cybernetics, pp. 3976–3980 (2009)
Li, G., Niu, P., Duan, X., Zhang, X.: Fast learning network: a novel artificial neural network with a fast learning speed. Neural Comput. Appl. 24(7–8), 1683–1695 (2014)
Guang-Bin, H., Qin-Yu, Z., Chee-Kheong, S.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference, vol. 2, pp. 985–990. August 2004
Smola, A.J., Schölkopf, B.: Learning with Kernels. February 2002
Flynn, H., Cameron, S.: Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, vol. 226 (2013)
Trelea, I.C.: The particle swarm optimization algorithm: Convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)
J. Blondin, “Particle swarm optimization: A tutorial,” … Site Http//Cs. Armstrong. Edu/Saad/Csci8100/Pso Tutor. …, pp. 1–5, 2009
Sengupta, A., Bhadauria, S., Mohanty, S.P.: TL-HLS: Methodology for Low Cost Hardware Trojan Security Aware Scheduling with Optimal Loop Unrolling Factor during High Level Synthesis. IEEE Trans. Comput. Des. Integr. Circuits Syst. 36(4), 660–673 (2017)
Mishra, V.K., Sengupta, A.: Swarm-inspired exploration of architecture and unrolling factors for nested-loop-based application in architectural synthesis. Electron. Lett. 51(2), 157–159 (2015)
Sengupta, A., Bhadauria, S.: User power-delay budget driven PSO based design space exploration of optimal k-cycle transient fault secured datapath during high level synthesis. In: Proceedings of the International Symposium Quality Electronic Design ISQED, vol. 2015, no. 6, pp. 289–292 (2015)
Mishra, V.K., Sengupta, A.: MO-PSE: Adaptive multi-objective particle swarm optimization based design space exploration in architectural synthesis for application specific processor design. Adv. Eng. Softw. 67, 111–124 (2014)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp. 69–73 (1998)
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Ali, M.H., Zolkipli, M.F. (2019). Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2018. Advances in Intelligent Systems and Computing, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-00979-3_15
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