Abstract
The Distributed Denial of Service (DDoS) attacks affect the availability of Web services for an indeterminate period of time, flooding the company’s servers with fraudulent requests and denying requests from legitimate users, generating economic losses by unavailable rendered services. Therefore, the aim of this paper is to show the process of detection prototype DDoS attacks using a supervised learning model by Support Vector Machines (SVM), which captures network traffic, filters HTTP headers, normalizes the data on the basis of the operational variables: rate of false positives, rate of false negatives, rate of classification and then sends the information to corresponding SVM’s training and testing sets. The results show that the proposed DDoS SVM prototype has high detection accuracy (99 %) decrease of the false positives and false negatives rates compared to conventional detection models.
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
Preview
Unable to display preview. Download preview PDF.
References
Keizer, G.: Garden-variety DDoS attack knocks North Korea off the Internet. Recovered March 13, 2015 (2014). http://www.computerworld.com/article/2862652/garden-variety-ddos-attack-knocks-north-korea-off-the-internet.html
Chan, A., Ng, W., Yeung, D., Tsang, E.C.: Refinement of rule-based intrusion detection system for denial of service attacks by support vector machine. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4252–4256 (2004)
Kartalopoulos, S.: Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications, 1st edn. Wiley-IEEE Press (1996)
Liu, W.-T.: Research on intrusion detection rules based on XML in distributed IDS. In: International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1400–1403, 12 de 07 de 2008
Mukkamala, S., Sung, A.: Detecting denial of service attacks using support vector machines. In: The 12th IEEE International Conference on Fuzzy Systems, FUZZ 2003, vol. 2, pp. 1231–1236 (2003)
Seufert, S., O’ Brien, D.: Machine Learning for Automatic Defence Against Distributed Denial of Service Attacks. In: IEEE International Conference on Communications, ICC 2007, pp. 1217–1222, 24–28 de Junio de 2007
Subbulakshmi, T., Shalinie, S., GanapathiSubramanian, V., BalaKrishnan, K., AnandKumar, D., Kannathal, K.: Detection of DDoS attacks using Enhanced Support Vector Machines with real time generated dataset. In: 2011 Third International Conference on Advanced Computing (ICoAC), pp. 17–22, 14.16 de Diciembre de 2011
Isaza, G.A., Castillo, L.F., Trujillo, M.L., Marulanda, C.E.: Modelo híbrido de neuroclasificación y clustering en el problema de detección de intrusiones. Vector, 69–77 (2012)
Isaza, G.A., Castillo, A., Lopez, M.F., Castillo, L.: Towards Ontology-based intelligent model for Intrusion Detection and Prevention. Journal of Information Assurance and Security 5(2), 376 (2010)
Saied, A., Overill, R.E., Radzik, T.: Artificial Neural Networks in the Detection of Known and Unknown DDoS Attacks: Proof-of-Concept. In: Communications in Computer and Information Science, vol. 430, pp. 300–320. Springer-Verlag, Heidelberg (2014). doi:10.1007/978-3-319-07767-3_28
Kacha, C., Shevade, K.A.: Comparison of Different Intrusion Detection and Prevention Systems. Intl. Journal of Emerging Technology and Advanced. Engineering 2(12), 243–245 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Hoyos Ll, M.S., Isaza E, G.A., Vélez, J.I., Castillo O, L. (2016). Distributed Denial of Service (DDoS) Attacks Detection Using Machine Learning Prototype. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-40162-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40161-4
Online ISBN: 978-3-319-40162-1
eBook Packages: EngineeringEngineering (R0)