Abstract
Rapid development of technologies not only makes life easier, but also reveals a lot of security problems. Developing and changing of attack types affect many people, organizations, companies etc. Therefore, intrusion detection systems have been developed to avoid financial and emotional loses. In this paper, we used CICIDS2017 dataset which consist of benign and the most cutting-edge common attacks. Best features are selected by using Fisher Score algorithm. Real world data extracted from the dataset are classified as DDoS or benign with using Support Vector Machine (SVM), K Nearest Neighbour (KNN) and Decision Tree (DT) algorithms. As a result of the study, 0,9997%, 0,5776%, 0,99% success rates were achieved respectively.
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
References
Ibrahimi, K., Ouaddane, M.: Management of intrusion detection systems based-KDD99: analysis with LDA and PCA. In: 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–6. IEEE (2017)
Moustafa, N., Slay, J.: The significant features of the UNSW-NB15 and the KDD99 data sets for network intrusion detection systems. In: 2015 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), pp. 25–31. IEEE (2015)
Sun, L., Anthony, T.S., Xia, H.Z., Chen, J., Huang, X., Zhang, Y.: Detection and classification of malicious patterns in network traffic using Benford’s law. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 864–872. IEEE (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), pp. 565–568. IEEE (2017)
Raja, M.C., Rabbani, M.M.A.: Combined analysis of support vector machine and principle component analysis for IDS. In: IEEE International Conference on Communication and Electronics Systems, pp. 1–5 (2016)
UNB Canadian Cyber Security, Intrusion Detection Evaluation Dataset (CICIDS2017). http://www.unb.ca/cic/datasets/ids-2017.html
Lunt, T.F.: Automated audit trail analysis and intrusion detection: a survey. In Proceedings of the 11th National Computer Security Conference, vol. 353 (1988)
Denning, D.E.: An intrusion-detection model. IEEE Trans. Softw. Eng. 2, 222–232 (1987)
Crosbie, M., Spafford, E.H.: Defending a computer system using autonomous agents (1995)
Endler, D.: Intrusion detection. Applying machine learning to Solaris audit data. In: 1998 Proceedings of 14th Annual Computer Security Applications Conference, pp. 268–279. IEEE (1998)
Patcha, A., Park, J.M.: An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Gu, Q., Li, Z., Han, J.: Generalized fisher score for feature selection (2012). arXiv preprint arXiv:1202.3725
Acknowledgement
This work is also a part of the M.Sc. thesis titled Performance Analysis of Log Based Intrusion Detection Systems Istanbul University, Institute of Physical Sciences.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Aksu, D., Üstebay, S., Aydin, M.A., Atmaca, T. (2018). 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) Computer and Information Sciences. ISCIS 2018. Communications in Computer and Information Science, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-00840-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-00840-6_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00839-0
Online ISBN: 978-3-030-00840-6
eBook Packages: Computer ScienceComputer Science (R0)