Abstract
This article presents the advances obtained in a research on the application of artificial intelligence (AI) techniques for the detection of denial of service (DoS). The investigation begins with the analysis of Machine Learning and Deep Learning techniques used to recognize DoS attacks, and then continues with the selection, training and classification of an algorithm for DoS detection. From the work carried out, it was possible to identify the artificial neural network Deep Feed Forward, which is a Deep Learning (DL) algorithm, which shows a very promising behavior to detect DoS attacks. For model training, the CICDDoS2019 data set was adapted, this data set contains twelve types of packages; eleven are DoS attacks and the twelfth belongs to benign or normal packets. The precision obtained was 0.7293, for the DL in-put model that recognizes 11 types of DoS attacks.
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
Sungur Unal, A., Hacibeyoglu, H.: Detection of DDOS attacks in network traffic using deep learning. In: International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2018) (2018)
Ahlgren, M.: websitehostingrating, 23 Marzo 2021. https://www.websitehostingrating.com/es/cybersecurity-statistics-facts/#cybersecurity-statistics
Alom, M.Z., Taha, T.M.: Network intrusion detection for cyber security using unsupervised deep learning approaches. Dep. Electr. Comput. Eng., 63–69 (2017)
Amarasinghe, K., Kenney, K., Manic, M.: Toward explainable deep neural network based anomaly detection. In: 2018 11th International Conference on Human System Interaction (HSI) (2018)
Amma, N.G., Subramanian, S.: VCDeepFL vector convolutional deep feature learning approach for identification of known and unknown denial of service attacks. In: TENCON 2018-2018 IEEE Region 10 Conference (2018)
Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., Marchetti, M.: On the effectiveness of machine and deep learning for cyber security. In: 10th International Conference on Cyber Conflict (2018)
Barik, K., Priyadarshini, R.: A deep learning based intelligent framework to mitigate DDoS attack in fog environment. J. King Saud Univ. Comput. Inf. Sci. (2019)
Chiba, Z., Abghour, N., Moussaid, K., omri, A.E., Rida, M.: Intelligent approach to build a deep neural network based IDS for cloud environment using combination of machine learning algorithms. Comput. Secur., 86, 291–317 (2019)
Chockwanich, N., Visoottiviseth, V.: Intrusion detection by deep learning with tensorflow. In: 2019 21st International Conference on Advanced Communication Technology (ICACT) (2019)
Computerworld.: Computerworld. https://computerworld.co/aumentan-ataques-de-denegacion-de-servicios/. Accessed 11 July 2020
Imamverdiyev, Y., Abdullayeva, F.: Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine. Research Gate (2018)
Islam, A.A.: Detection of various denial of service and distributed denial. In: Proceedings of 2009 12th International Conference on Computer and Information Technology, Dhaka, Bangladesh, pp. 603–607. ICCIT 2009 (2009)
Kasongo, S.M., Sun, Y.: A deep long short-term memory based classifier for wireless intrusion detection system. ICT Express 6, 98–103 (2019)
Khuphiran, P., Leelaprute, P., Uthayopas, P., Ichikawa, K., Watanakeesunt, W.: Performance comparison of machine learning models for DDoS attacks detection. In: 2018 22nd International Computer Science and Engineering Conference (ICSEC) (2018)
Kim, T.-Y., Cho, S.-B.: Web traffic anomaly detection using C-LSTM neural networks. Expert Syst. Appl. 106, 66–76 (2018)
Liu, H., Lang, B., Liu, M., Yanb, H.: CNN and RNN based payload classification methods for attack detection. Knowl. Based Syst. 163, 322–341 (2019)
Bhuvaneswari Amma, N.G., Selvakumar, S.: Deep radial intelligence with cumulative incarnation approach for detecting denial of service attacks. Neurocomputing 340, 294–308 (2019)
Sharafaldin, I., Lashkari, A.H., Hakak, S., Ghorbani, A.A.: Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy (2019)
Siracusano, M., Shiales, S., Ghita, B.: Detection of LDDoS attacks based on TCP connection parameters. In: Global Information Infraestructure and Networking Symposium, vol. 6 (2018)
Thilina, A., et al.: Intruder detection using deep learning and association rule mining. In: IEEE International Conference on Computer and Information Technology (2016)
Yuan, X., C. Li: DeepDefense identifying DDoS attack via deep learning. In: Large-Scale Intelligent Systems Laboratory (2017)
Xing, Y., et al.: Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365–35381 (2018)
Xu, C., Shen, J., Du, X., Zhang, F.: An Intrusion detection system using a deep neural network with gated recurrent units. IEEE Access 6, 48697–48707 (2018)
Yadav, S., Subramanian, S.: Detection of application layer DDoS attack by feature learning using Stacked AutoEncoder. In: 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT) (2016)
Zargar, S.T., Joshi, J., Tipper, D.: A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE, pp. 2046–2069 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Cañola Garcia, J.F., Taborda Blandon, G.E. (2022). Implementing a Deep Learning Algorithm for Detection of Denial of Service Attacks. In: Gonzalez, E., Curiel, M., Moreno, A., Carrillo-Ramos, A., Páez, R., Flórez-Valencia, L. (eds) Advances in Computing. CCC 2021. Communications in Computer and Information Science, vol 1594. Springer, Cham. https://doi.org/10.1007/978-3-031-19951-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-19951-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19950-9
Online ISBN: 978-3-031-19951-6
eBook Packages: Computer ScienceComputer Science (R0)