Abstract
Along with the high-speed growth of the Internet, cyber-attacks are becoming even more frequent, so detecting network intrusions is essential for keeping network connections under control. However, in the modern big data environment, traditional methods do not meet the network requirements regarding adaptability and efficiency. Therefore, intelligent intrusion detection systems can only be built if an adequate data set is used. This way, a data set with a sizable amount of quality data can mimic real-time network traffic to train and test an intrusion detection system (IDS). The NSL-KDD data set is a refined version of its predecessor KDD‟99 data set. In this paper, the NSL-KDD data set is analyzed and used to study the effectiveness of various classification algorithms in detecting anomalies in network traffic patterns. The results show that the Random Forest algorithm provides the best results with accuracy, precision, recall and an F1 score of 99%.
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References
Yeo, L.H., Che, X., Lakkaraju, S.: Understanding modern intrusion detection systems: a survey. Eastern Michigan University, USA (2017)
Jyothsna, V.V.R.P.V., Prasad, V.R., Prasad, K.M.: A review of anomaly based intrusion detection systems. Int. J. Comput. Appl. 28(7), 26–35 (2011)
Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: Second IEEE International Conference on Computational Intelligence for Security and Defense Applications, pp. 53–58 (2009)
Vilela, D.W.F.L., Lotufo, A., Santos, C.R.: Fuzzy ARTM AP neural network IDS evaluation applied for real IEEE 802.11w data base. In: IEEE International Joint Conference on Neural Networks, pp. 1–7 (2018)
Tong, Z., Ying, H.: Application of frequent item set mining algorithm in IDS based on hadoop framework. In: IEEE Chinese Control and Decision Conference, pp. 1908–1911 (2018)
Hajare, S.A.: Detection of network attacks using big data analysis. Int. J. Recent Innov. Trends Comput. Commun. 4(5), 86–88 (2016)
Golman, V.: An efficient hybrid intrusion detection system based on C5.0 and SVM. Int. J. Database Theor. Appl. 7(2), 59–70 (2014)
Tanpure, S.S. et al.: Intrusion detection system in data mining using hybrid approach. Int. J. Comput. Appl., 0975–8887 (2016)
Ferrag, M.A., Maglaras, L., Moschoyiannis S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50(1) (2020)
James, D.: Introduction to Machine Learning with Python: a Guide for Beginners in Data Science, 1st edn. CreateSpace Independent Publishing Platform, USA (2018)
Joshi, A.V.: Machine Learning and Artificial Intelligence. Springer (2020)
Chen, X., Jeong, J.C.: Enhanced recursive feature elimination. In: Machine Learning and Applications Conference, pp 429–435 (2007)
Caswell, B., Beale, J., Baker, A.: Snort Intrusion Detection and Prevention Toolkit. Syngress. MA, Burlingthon (2007)
Ambati, S.B., Vidyarthi, D.: A brief study and comparison of open source intrusion detection system and tools. Int. J. Adv. Comput. Eng. Netw. 1(10), 26–32 (2013)
Hutchison, K.: Wireless Intrusion Detection Systems, SANS Institute. White Paper (2005)
Stallings, W.: Network Security Essentials: Applications and Standards, 6th edn. Pearson, USA (2017)
Stiawan, D., Shakhatreh, A.I., Idris, M.Y., Bakar, K.K.A., Abdullah, A.H.: Intrusion prevention system: a survey. J. Theor. Appl. Inf. Technol. 40(1) (2012)
Protić, D.D.: Review of KDD CUP ‘99, NSL-KDD and KYOTO 2006+ datasets. Military Tech. Courier 66(3), 580–596 (2018)
Madhavan, S.: Mastering Python for Data Science. Packt Publishing, UK (2015)
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Cholakoska, A., Shushlevska, M., Todorov, Z., Efnusheva, D. (2021). Analysis of Machine Learning Classification Techniques for Anomaly Detection with NSL-KDD Data Set. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Intelligent Systems. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-030-90321-3_21
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