Abstract
Dealing the Distributed Denial of Service (DDoS) attack is a continuing challenge in the field of network security. An Intrusion Detection System (IDS) is one of the solutions to detect the DDoS attack. The IDS system should always be updated with the attack disincentive to preserve the network security service. In this paper, we propose a new approach for anomaly detection using machine learning to secure the network and to determine the attack patterns. The major contribution is to create real-time dataset and to use the naive Bayes algorithm as a classifier for detecting and comparing its performance with the existing classifiers like random forest and J48 algorithm. The experimental results on the real-time dataset confirm that the proposed machine learning approach can effectively detect network anomalies with high detection rate and low false positive rate.
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
Tang, H., Cao, Z.: Machine Learning-based intrusion detection algorithms. J. Comput. Inf. Sys. 5(6), 1825–1831 (2009)
Patcha, A, Park, J.-M.: An overview of anomaly detection techniques: Existing solutions and latest technological trends. Comput. Netw. 51(12), 3448–3470 (2007)
Prajapati, N.M., Mishra, A., Bhanodia, P.: Literature survey-IDS for DDoS attacks. In: 2014 Conference on IT in Business, Industry and Government (CSIBIG), pp. 1–3. IEEE (2014)
Abid, K.: An efficient intrusion detection using J48 decision tree in KDDCUP99 dataset. Int. J. Emerging Technol. Adv. Eng. 6(2), (2016)
Tesfahun, A., Bhaskari, D.L.: Intrusion detection using random forests classifier with SMOTE and feature reduction. In: 2013 International Conference on IEEE Cloud & Ubiquitous Computing & Emerging Technologies (CUBE) pp. 127–132 (2013)
Martinez-Arroyo, M., Sucar, L.E.: Learning an optimal naive bayes classifier. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006 vol. 3, pp. 1236–1239. IEEE (2006)
Pandey, M., Sharma, V.K.: A decision tree algorithm pertaining to the student performance analysis and prediction. Int. J. Comput. Appl., 61(13), (2013)
Alkasassbeh, M., Al-Naymat, G., Hassanat, A.B., Almseidin, M.: Detecting distributed denial of service attacks using data mining techniques. Int. J. Adv. Comput. Sci. Appl., 7(1) (2016)
Kaur, R., Singh, S.: A survey of data mining and social network analysis based anomaly detection techniques. Egyptian Informatics J. 1(2), 199–216 (2016)
Benferhat, S., Kenaza, T., Mokhtari, A.: A naive bayes approach for detecting coordinated attacks, In: 32nd Annual IEEE International Computer Software and Applications, 2008. COMPSAC’08. pp. 704–709 (2008)
Panda, M., Patra, M.R.: Network intrusion detection using naive bayes. Int. J. Comput. Sci. Network Security 7(12), 258–263 (2007)
Shamshirband, S., et al.: Anomaly detection using fuzzy Q-learning algorithm. Acta Polytech. Hungarica 11(8), 5–28 (2014)
Wireshark: https://wireshark.en.softonic.com
Eldos, T., Siddiqui, M., Kanan, A.: The KDD’99 Dataset: statistical analysis for feature selection. J. Data Mining Knowledge Discovery 3(3), 88–90 (2012)
Song, F., Guo, Z., Mei, D.: Feature selection using principal component analysis. In: 2010 International Conference On System Science, Engineering Design and Manufacturing Informatization (ICSEM), vol. 1, pp. 27–30. IEEE, (2010)
Amudha, P., Rauf, H.A: Performance analysis of data mining approaches in intrusion detection. In: 2011 International Conference on IEEE Process Automation, Control and Computing (PACC)
Domingos, P.., Pazzani, M.: Beyond Independence: conditions for the Optimality of the Simple Bayesian Classifier. Available at: http://www.ics.uci.edu/~pazzani/Publications/mlc96-pedro.pdf
Frank, E., Trigg, L., Holmes, G., Witten, I.A.: Naïve Bayes for Regression. Mach. Learn. 41(1), 1–20 (1999)
Zhang, H.: The Optimality of Naïve Bayes. American Association for Artificial Intelligence (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Narasimha Mallikarjunan, K., Bhuvaneshwaran, A., Sundarakantham, K., Mercy Shalinie, S. (2019). DDAM: Detecting DDoS Attacks Using Machine Learning Approach. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_21
Download citation
DOI: https://doi.org/10.1007/978-981-13-1132-1_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1131-4
Online ISBN: 978-981-13-1132-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)