Abstract
Securing the network from intrusions becomes a more challenging task to conduct for system administrators, and the need for a more powerful and efficient intrusion detection system emerges with the continuous development of cyber-attacks exploring various methods and techniques. We propose the usage of artificial intelligence to build a sophisticated Network Intrusion Detection System able to be trained/self-trained to detect malicious network traffic. The aim of this paper is to present a new IDS model based on machine learning approach to detect malicious traffic and protect the network from cyber-attacks. The usage of machine learning will allow better accuracy in detection and faster response time. To implement and measure the performance of our model, we used NSL-KDD dataset, after the preprocessing phase, (cleaning, normalization …), we used the most relevant attributes for the classification process based on CfsSubsetEval technique with BestFirst approach as an attribute selection algorithm for removing the redundant attributes to allow the usage of the most pertinent attributes of the dataset. Based on the comparative evaluation of three algorithms (K-means, AdaBoost and Multilayer Perceptron), the experimental results show that the MLP algorithm provides a high detection rate and reduces false alarm rate. Finally, a set of principles is concluded, which will set path for future research for implementing an efficient and performant IDS. To help researchers in the selection of IDS, several recommendations are provided with future directions for this research.
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References
Liu, G., Zhang, J.: CNID: research of network intrusion detection based on convolutional neural network. Discrete Dyn. Nat. Soc. 2020 (2020)
Panigrahi, R., Borah, S.: A statistical analysis of lazy classifiers using Canadian Institute of Cybersecurity datasets. In: Advances in Data Science and Management, pp. 215–222. Springer, Singapore (2020)
Jaber, F.Kh., Al-Zwainy, F.M.S., Hachem, S.W.: Optimizing of predictive performance for construction projects utilizing support vector machine technique. Cogent Eng. 6(1), 1685860 (2019)
Jain, R., et al.: Assessing risk in life insurance using ensemble learning. J. Intell. Fuzzy Syst. 37(2), 2969–2980 (2019)
Toneva, D.H., et al.: Data mining for sex estimation based on cranial measurements. Forensic Sci. Int. 315, 110441 (2020)
Baglaeva, E., et al.: Recognition of chromium distribution features in different urban soils by multilayer perceptron. In: AIP Conference Proceedings, vol. 2040, no. 1. AIP Publishing LLC (2018)
Jaffuel, D., et al.: Patterns of adaptive servo-ventilation settings in a real-life multicenter study: pay attention to volume! Respir. Res. 21(1), 1–13 (2020)
Hastie, T., et al.: Multi-class adaboost. Stat. Interface 2(3), 349–360 (2009)
Kevric, J., Jukic, S., Subasi, A.: An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Comput. Appl. 28(1), 1051–1058 (2017)
Devan, P., Khare, N.: An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Comput. Appl. 32, 12499–12514 (2020)
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Advances in Neural Information Processing Systems (2001)
Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, no. 22 (2001)
Menard, S.: Applied Logistic Regression Analysis, vol. 106. Sage, Thousand Oaks (2002)
Kunhare, N., Tiwari, R., Dhar, J.: Particle swarm optimization and feature selection for intrusion detection system. Sadhana 45(1) (2020)
Tama, B.A., Rhee, K.-H.: An in-depth experimental study of anomaly detection using gradient boosted machine. Neural Comput. Appl. 31(4), 955–965 (2019)
Zhou, J., Shi, X., Li, X.: Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J. Vib. Control 22(19), 3986–3997 (2016)
Svetnik, V., et al.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)
Aljawarneh, S., Yassein, M.B., Aljundi, M.: An enhanced J48 classification algorithm for the anomaly intrusion detection systems. Clust. Comput. 22(5), 10549–10565 (2019)
Dhanabal, L., Shantharajah, S.P.: A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 4(6), 446–452 (2015)
Belouch, M., Elhadaj, S., Idhammad, M.: A hybrid filter-wrapper feature selection method for DDoS detection in cloud computing. Intell. Data Anal. 22(6), 1209–1226 (2018)
Shamshirband, S., et al.: Co-FQL: anomaly detection using cooperative fuzzy Q-learning in network. J. Intell. Fuzzy Syst. 28(3), 1345–1357 (2015)
Yi, Z., et al.: A navigation method for mobile robots using interval type-2 fuzzy neural network fitting Q-learning in unknown environments. J. Intell. Fuzzy Syst. 37(1), 1113–1121 (2019)
Hachmi, F., Boujenfa, K., Limam, M.: Enhancing the accuracy of intrusion detection systems by reducing the rates of false positives and false negatives through multi-objective optimization. J. Netw. Syst. Manage. 27(1), 93–120 (2019)
Castillo, O., et al.: Fuzzy parameter adaptation in genetic algorithms for the optimization of fuzzy integrators in modular neural networks for multimodal biometry. Computación y Sistemas 24(3) (2020)
Sánchez, D., Melin, P., Castillo, O.: Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition. J. Intell. Fuzzy Syst. 38(3), 3229–3252 (2020)
Cui, T., et al.: Data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises. Int. J. Syst. Sci. 51(9), 1669–1684 (2020)
Hu, B., Bi, L., Dai, S.: Information distances versus entropy metric. Entropy 19(6), 260 (2017)
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Wadiai, Y., El Mourabit, Y., Baslam, M. (2021). Machine Learning for Intrusion Detection: Design and Implementation of an IDS Based on Artificial Neural Network. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2020. Advances in Intelligent Systems and Computing, vol 1372. Springer, Cham. https://doi.org/10.1007/978-3-030-73603-3_19
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