Abstract
Anomaly Detection is an important field of research in the present age of ubiquitous computing. Increased importance in Network Monitoring and Security due to the growing Internet is the driving force for coming up with new techniques for detecting anomalies in network behaviour. In this paper, Artificial Neural Network (ANN) model optimised with a hybrid of Particle Swarm Optimiser (PSO) and Differential Evolution (DE) is proposed to monitor the behaviour of the network and detect any anomaly in it. We have considered two subsets of 2000 and 10000 dataset size of the NSL KDD dataset for training and testing our model and the results from this model is compared with the traditional ANN-PSO algorithm, and one of the existing variants of PSO-DE algorithm. The performance measures used for the analysis of results are the training time, precision, recall and f1-score.
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References
Yang, H., Xie, F., Lu, F.: Research on network anomaly detection based on clustering and classifier. In: 2006 International Conference on Computational Intelligence and Security, Guangzhou, pp. 592–597 (2006)
Common Types of Network Attacks - Microsoft Docs
Holm, H.: Signature based intrusion detection for zero-day attacks. In: 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, pp. 4895–4904 (2014)
Zhang, W., Yang, Q., Geng, Y.: A survey of anomaly detection methods in networks. In: 2009 International Symposium on Computer Network and Multimedia Technology, Wuhan, pp. 1–3 (2009)
Andropov, S., Guirik, A., Budko, M., Budko, M.: Network anomaly detection using artificial neural networks. In: 2017 20th Conference of Open Innovations Association (FRUCT), St. Petersburg (2017)
Almansob, S.M., Lomte, S.S.: Addressing challenges for intrusion detection system using naive Bayes and PCA algorithm. In: 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, pp. 565–568 (2017)
Mabu, S., Chen, C., Lu, N., Shimada, K., Hirasawa, K.: An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(1), 130–139 (2011)
Kevric, J., Jukic, S., Subasi, S.: An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Comput. Appl., 1–8 (2016)
Lei, Y.: Network anomaly traffic detection algorithm based on SVM. In: 2017 International Conference on Robots & Intelligent System (ICRIS), Huai’an, pp. 217–220 (2017). https://doi.org/10.1109/ICRIS.2017.61
Thakong, M., Wongthanavasu, S.: Packet header anomaly detection using bayesian belief network. ECTI Trans. Comput. Inf. Technol. 3(1), 26–30 (2007)
Li, W., Duan, M., Chen, Y.: Network anomaly detection based on MRMHC-SVM algorithm. In: 2008 IEEE International Multitopic Conference, Karachi, pp. 307–312 (2008)
Al-Janabi, S.T.F., Saeed, H.A.: A neural network based anomaly intrusion detection system. In: 2011 Developments in E-systems Engineering, Dubai, pp. 221–226 (2011)
Su, M.-Y.: Real-time anomaly detection systems for Denial-of-Service attacks by weighted k-nearest-neighbor classifiers. Expert Syst. Appl. 38(4), 3492–3498 (2011)
Singh, S., Silakari, S.: An ensemble approach for feature selection of Cyber Attack Dataset. Int. J. Comput. Sci. Inf. Secur. P12-(IJCSIS), 6(2), 297–302 (2009)
Peddabachigari, S., Abraham, A., Grosan, C., Thomas, J.: Modeling intrusion detection system using hybrid intelligent systems. J. Netw. Comput. Appl 30(1), 114–132 (2007)
Lin, W.-C., Ke, S.-W., Tsai, C.-F.: CANN: an intrusion detection system based on combining cluster centers and nearest neighbors. Knowl. Based Syst. 78, 13–21 (2015)
Li, H.: Research and Implementation of an anomaly detection model based on clustering analysis. In: 2010 International Symposium on Intelligence Information Processing and Trusted Computing, Huanggang, pp. 458–462 (2010)
Leon, E., Nasraoui, O., Gomez, J.: Anomaly detection based on unsupervised niche clustering with application to network intrusion detection. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), vol. 1, pp. 502–508 (2004)
Jidiga, G.R., Sammulal, P.: Anomaly detection using machine learning with a case study. In: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, pp. 1060–1065 (2014)
Callegari, C., Giordano, S., Pagano, M.: Neural network based anomaly detection. In: 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Athens, pp. 310–314 (2014)
Han, S.-J., Cho, S.-B.: Evolutionary neural networks for anomaly detection based on the behavior of a program. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(3), 559–570 (2005)
Lima, M.F., Sampaio, L.D.H., Zarpelao, B.B., Rodrigues, J.J.P.C., Abrao, T., Proenca Jr., M.L.: Networking anomaly detection using DSNs and particle swarm optimization with re-clustering. In: 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, Miami, FL, pp. 1–6 (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, vol. 4, pp. 1942–1948 (1995)
Koohi, I., Groza, V.Z.: Optimizing particle swarm optimization algorithm. In: 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), Toronto, ON, pp. 1–5 (2014)
Koehrsen, W.: “Beyond Accuracy: Precision and Recall” - Towards Data Science
Storn, R., Price, K.: Differential Evolution-a Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces. ICSI Berkeley, Berkeley (1995)
Lin, Y.-C., Hwang, K.-S., Wang, F.-S.: Co-evolutionary hybrid differential evolution for mixed-integer optimization problems. Eng. Optim. 33(6), 663–682 (2001)
Storn, R.: On the usage of differential evolution for function optimization. In: NAFIPS 1996 Biennial Conference of the North American Fuzzy Information Processing Society, pp. 519–523. IEEE (1996)
Elsayed, S., Sarker, R., Slay, J.: Evaluating the performance of a differential evolution algorithm in anomaly detection. In: 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, pp. 2490–2497 (2015)
Teekeng, W., Unkaw, P.: A new hybrid model of PSO and DE algorithm for data classification. In: 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Kanazawa, pp. 47–51 (2017)
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Rithesh, K., Gautham, A.V., Chandra Sekaran, K. (2019). Network Anomaly Detection Using Artificial Neural Networks Optimised with PSO-DE Hybrid. In: Thampi, S., Madria, S., Wang, G., Rawat, D., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2018. Communications in Computer and Information Science, vol 969. Springer, Singapore. https://doi.org/10.1007/978-981-13-5826-5_19
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DOI: https://doi.org/10.1007/978-981-13-5826-5_19
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