Abstract
The growth of the Internet and the opening of systems have led to an increasing number of attacks on computer networks. Security vulnerabilities are increasing, in the design of communication protocols as well as in their implementation. On another side, the knowledge, tools and scripts, to launch attacks, become readily available and more usable. Hence, the need for an intrusion detection system (IDS) is also more apparent. This technology consists in searching for a series of words or parameters characterizing an attack in a packet flow. Intrusion Detection Systems has become an essential and critical component in an IT security architecture. An IDS should be designed as part of a global security policy. The objective of an IDS is to detect any violation of the rules according to the local security policy, it thus makes it possible to report attacks. This last multi-faceted, difficult to pin down when not handled, but most of the work done in this area remains difficult to compare, that's why the aim of our article is to analyze and compare intrusion detection techniques with several machine learning algorithms. Our research indicates which algorithm offers better overall performance than the others with the IDS field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Modi, U., Jain, A.: An improved method to detect intrusion using machine learning algorithms. Inf. Eng. Int. J. (IEIJ) 4(2), 17–29 (2016). https://doi.org/10.5121/ieij.2016.4203
Aksu, D., Üstebay, S., Aydin, M.A., Atmaca, T.: Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. In: Czachórski, T., Gelenbe, E., Grochla, K., Lent, R. (eds.) ISCIS 2018. CCIS, vol. 935, pp. 141–149. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00840-6_16
Othman, S.M., Ba-Alwi, F.M., Alsohybe, N.T., Al-Hashida, A.Y.: Intrusion detection model using machine learning algorithm on Big Data environment. J. Big Data 5(1), 1–12 (2018). https://doi.org/10.1186/s40537-018-0145-4
Anwar, S., et al.: From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions. Algorithms 10(2), 39 (2017). https://doi.org/10.3390/a10020039
Gao, J., Chai, S., Zhang, B., Xia, Y.: Research on network intrusion detection based on incremental extreme learning machine and adaptive principal component analysis. Energies 12(7), 1223 (2019). https://doi.org/10.3390/en12071223
Gu, J., Wang, L., Wang, H., Wang, S.: A novel approach to intrusion detection using SVM together with increased feature. Comput. Secur. (2019). https://doi.org/10.1016/j.cose.2019.05.022
Zhang, H., Peng, H., Yang, Y.: Nearest neighbors based approach to density peaks intrusion detection. Chaos, Solitons Fractals 110, 33–40 (2018). https://doi.org/10.1016/j.chaos.2018.03.010
Mukherjee, S., Sharma, N.: Intrusion detection using naive bayes classify with feature reduction. Procedia Technol. 4, 119–128 (2012). https://doi.org/10.1016/j.protcy.2012.05.017
Jamal, H., Mishra, A.: An actual intrusion detection based on support vector framework machine using NSL - KDD dataset. Indian J. Comput. Sci. Eng. (IJCSE) 8(6), 703–713, December 2017–January 2018. e-ISSN: 0976–5166
Rai, K., Devi, M.S., Guleria, A.: Decision tree-based algorithm for intrusion detection. Int. J. Adv. Netw. Appl. 7, 2828–2834 (2016)
Mani, L., Vidya, P.: A novel intrusion detection model for mobile ad -hoc networks using CP – KNN. Int. J. Comput. Netw. Commun. 6 (2014). https://doi.org/10.5121/ijcnc.2014.651
Liao, Y., Rao, V.: Use of K-nearest neighbor classifier for intrusion detection. Comput. Secur. 21, 439–448 (2002). https://doi.org/10.1016/S0167-4048(02)00514-X
Charles, B.: Skybox security. In: 2019 Vulnerability and Threat Trends, 29 March 2019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Elaeraj, O., Leghris, C., Renault, É. (2021). Performance Evaluation of Some Machine Learning Algorithms for Security Intrusion Detection. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2020. Lecture Notes in Computer Science(), vol 12629. Springer, Cham. https://doi.org/10.1007/978-3-030-70866-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-70866-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70865-8
Online ISBN: 978-3-030-70866-5
eBook Packages: Computer ScienceComputer Science (R0)