Abstract
The term of online attacks appeared in public space in the area of computer networks long ago. The effects of these actions can be difficult to rectify and also very expensive. For early detection of such attacks, one can use different methods to analyze the input data generated by the network communication interfaces. The article presented the results of the research on effectiveness of data mining techniques in the detection of DDoS attacks on the selected network resources.
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References
1. Ashari A., Paryudi I., Tjoa M.: Performance Comparison between Nave Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation. International Journal of Advanced Computer Science and Applications, Vol. 4, No. 11, pp. 33–39, Bradford UK, 2013.
2. Bandara K.R.W.V., et al.: Preventing DDoS attack using Data mining Algorithms. International Journal of Scientic and Research Publications, Vol. 6, Issue 10, pp. 390–400, 2016.
3. BISHOP C. M., Pattern Recognition and Machine Learning, Springer, 2006.
4. Zhong R., Guangxue Y.: DDoS Detection System Based on Data Mining. Proceedings of the ISNNS 10, pp. 062–065, Jinggangshan, P. R. China, 2010.
5. Czyczyn-Egird D., Wojszczyk R.: Determining the Popularity of Design Patterns Used by Programmers Based on the Analysis of Questions and Answers on Stackoverow.com Social Network. 23rd Conference Computer Networks, series Communications in Computer and Information Science, Springer, Vol. 608, pp. 421–433, Brunow, 2016.
6. Gorski G.: Novel Multistage authorization Protocol. Information Systems Architecture and Technology: Service Oriented Networked Systems. Wroclaw University of Technology. pp. 221–230, Wroclaw, 2011.
7. Hassanat A. B., et al.: Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach. International Journal of Computer Science and Information Security, Vol. 12, No. 8, pp. 33–39, Ptitsburgh USA, 2014.
8. HeeKyoung Yi, et al.: DDoS Detection Algorithm Using the Bidirectional Session. 18th Conference Computer Networks, series Communications in Computer and Information Science, Vol. 160, pp. 191–203, Ustron, 2011.
11. Troelsen A.: Pro C# 2008 and the .NET 3.5 Platform. Apress, New York, 2007.
12. Wojszczyk R.: The Process of Verifying the Implementation of Design Patterns Used Data Models. Advances in Intelligent Systems and Computing, Vol. 521, pp. 103–116, 2017.
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Czyczyn-Egird, D., Wojszczyk, R. (2018). The effectiveness of data mining techniques in the detection of DDoS attacks. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_7
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DOI: https://doi.org/10.1007/978-3-319-62410-5_7
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