Abstract
Intrusion Detection Systems (IDS) effort to detect intrusion and misuse attack computer systems by assembling and examining data of computer networks. The IDS is usually examining huge traffic data based on Machine Learning (ML) algorithms to identify harmful changes or attacks, however, which algorithm can manifest the best performance is an issue to be investigated. ML-IDS requires to decrease false alarm and increase true alarm rates. In this work, three tree-based ML algorithms which are Decision Tree (DT), Decision Jungle (DJ), and Decision Forest (DF) have been tested and evaluated in an IDS model. The main objective of this work is to compare the performance of the three algorithms based on accuracy, precision and recall evaluation criteria. The Knowledge Discovery in Databases (KDD) methodology and Kaggle intrusion detection dataset are used in the testing. The results show that the DF achieves the highest overall accuracy of 99.83%, the DJ achieves the second highest overall accuracy of 99.74% and the DT achieves the lowest overall accuracy of 95.59%. The obtained results can serve as a benchmark in the evaluation of advanced IDS.
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
Khalaf, B.A., Mostafa, S.A., Mustapha, A., Mohammed, M.A., Abduallah, W.M.: Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods. IEEE Access 7, 51691–51713 (2019)
Jubair, M.A., et al.: Bat optimized link state routing protocol for energy-aware mobile ad-hoc networks. Symmetry 11(11), 1409 (2019)
Richariya, V., Singh, U.P., Mishra, R.: Distributed approach of intrusion detection system: survey. Int. J. Adv. Comput. Res. 2(4), 358 (2012)
Aburomman, A.A., Reaz, M.B.I.: A novel SVM-kNN-PSO ensemble method for intrusion detection system. Appl. Soft Comput. 38, 360–372 (2016)
Farnaaz, N., Jabbar, M.A.: Random forest modeling for network intrusion detection system. Procedia Comput. Sci. 89(1), 213–217 (2016)
Khalaf, B.A., Mostafa, S.A., Mustapha, A., Abdullah, N.: An adaptive model for detection and prevention of DDoS and flash crowd flooding attacks. In: 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR), pp. 1–6. IEEE, August 2018
Elmasry, W., Akbulut, A., Zaim, A.H.: Empirical study on multiclass classification-based network intrusion detection. Comput. Intell. 35(4), 919–954 (2019)
Ishak, A.M., Mustapha, A., Idrus, S.Z.S., Abd Wahab, M.H., Mostafa, S.A.: Correlation impact by random forest towards prediction of phishing website. In: IOP Conference Series: Materials Science and Engineering, vol. 917, no. 1, p. 012043. IOP Publishing (2020)
Razali, N., Mostafa, S.A., Mustapha, A., Abd Wahab, M.H., Ibrahim, N.A.: Risk factors of cervical cancer using classification in data mining. In: Journal of Physics: Conference Series, vol. 1529, no. 2, p. 022102. IOP Publishing, April 2020
Rajagopal, S., Hareesha, K.S., Kundapur, P.P.: Performance analysis of binary and multiclass models using azure machine learning. International Journal of Electrical & Computer Engineering (2088-8708), 10 (2020)
Razali, N., Mustapha, A., Abd Wahab, M.H., Mostafa, S.A., Rostam, S.K.: A data mining approach to prediction of liver diseases. In: Journal of Physics: Conference Series, vol. 1529, no. 3, p. 032002. IOP Publishing, April 2020
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)
Shamim, A., Balakrishnan, V., Kazmi, M., Sattar, Z.: Intelligent data mining in autonomous heterogeneous distributed and dynamic data sources. In: 2nd International Conference on Innovations in Engineering and Technology (ICCET’2014), pp. 19–20, Sept 2014
Gao, X., Shan, C., Hu, C., Niu, Z., Liu, Z.: An adaptive ensemble machine learning model for intrusion detection. IEEE Access 7, 82512–82521 (2019)
Ghosh, P., Mitra, R.: Proposed GA-BFSS and logistic regression based intrusion detection system. In: Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–6. IEEE, February 2015
Stibor, T., Timmis, J., Eckert, C.: A comparative study of real-valued negative selection to statistical anomaly detection techniques. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 262–275. Springer, Heidelberg (2005). https://doi.org/10.1007/11536444_20
Li, Y., Qiu, R., Jing, S.: Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid. PLoS ONE 13(2), e0192216 (2018)
Shakya, S., Kaphle, B.R.: Intrusion detection system using back propagation algorithm and compare its performance with self organizing map. J. Adv. Coll. Eng. Manag. 1, 127 (2016)
Microsoft Azure Machine Learning Studio. https://studio.azureml.net/. Accessed on June 2016
Introducing Kaggle Simulations. https://www.kaggle.com/what0919/intrusion-detection. Accessed on 2019
Micro Average vs Macro average Performance in a Multiclass classification setting, Data Science (2018). https://datascience.stackexchange.com/questions/15989/micro-average-vs-macro-average-performance-in-a-multiclass-classification-settin
Khalaf, B.A., et al.: A simulation study of syn flood attack in cloud computing environment. AUS J. 1–10, 2019 (2019)
Al-Ta’i, Z.T.M., Abass, J.M., Abd Al-Hameed, O.Y.: Image steganography between Firefly and PSO Algorithms. Int. J. Comput. Sci. Inform. Secur. 15(2), 9 (2017)
Babatunde, O.S., Ahmad, A.R., Mostafa, S.A., khalaf, B.A., Fadel, A.H., Shamala, P.: A smart network intrusion detection system based on network data analyzer and support vector machine. In: International Journal of Emerging Trends in Engineering Research, vol. 8, no. 1, pp. 213–220 (2020)
Fadel, H., Hameed, R.S., Hasoon, J.N., Mostafa, S.A.: A Light-weight ESalsa20 Ciphering based on 1D Logistic and Chebyshev Chaotic Maps. Solid State Technol. 63(1), 1078–1093 (2020)
Acknowledgement
This paper is supported by Universiti Tun Hussein Onn Malaysia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zulhilmi, A., Mostafa, S.A., Khalaf, B.A., Mustapha, A., Tenah, S.S. (2021). A Comparison of Three Machine Learning Algorithms in the Classification of Network Intrusion. In: Anbar, M., Abdullah, N., Manickam, S. (eds) Advances in Cyber Security. ACeS 2020. Communications in Computer and Information Science, vol 1347. Springer, Singapore. https://doi.org/10.1007/978-981-33-6835-4_21
Download citation
DOI: https://doi.org/10.1007/978-981-33-6835-4_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6834-7
Online ISBN: 978-981-33-6835-4
eBook Packages: Computer ScienceComputer Science (R0)