Abstract
Nowadays computer network traffic is increasing exponentially making the Internet vulnerable to huge number of security threats. Distributed Denial of Service (DDoS) attacks is one such threats that deny services to legitimate users. This necessitates a system for classifying DDoS attacks to protect the computing services provided to legitimate users. Recently, deep learning techniques have been proposed to detect DDoS attacks. The existing deep learning-based classification systems perform static detection of attacks failing to capture unknown attacks happening in the evolving traffic. The unknown attacks could be detected on the fly if a generalizable model is designed for each evolving class of network traffic. This is effectively represented in the proposed Deep Autonomous Learning (DAL) classifier to detect DDoS attacks in the traffic data streams. The proposed DAL network traffic classifier learns the features automatically using incremental learning with distilled cross entropy and classifies the evolving network traffic using softmax function. The proposed classification approach was tested by conducting experiments on benchmark datasets generated from network traffic. It is evident that the DAL network classification approach has the potential to classify the unknown DDoS attacks. Further, it is observed from the comparative analysis that the proposed DAL classifier outperforms the existing approaches.
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
Amma BN, Selvakumar S (2019) Deep radial intelligence with cumulative incarnation approach for detecting denial of service attacks. Neurocomputing 340:294–308. https://doi.org/10.1016/j.neucom.2019.02.047
Amma NGB, Subramanian S (2018) 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. IEEE, pp 0640–0645
Amma NB, Selvakumar S, Velusamy RL (2020) A statistical approach for detection of denial of service attacks in computer networks. IEEE Trans Netw Serv Manag 17(4):2511–2522. https://doi.org/10.1109/TNSM.2020.3022799
Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv:1503.02531
Idhammad M, Afdel K, Belouch M (2018) Semi-supervised machine learning approach for ddos detection. Appl Intell 1–16. https://doi.org/10.1007/s10489-018-1141-2
Istrate R, Malossi ACI, Bekas C, Nikolopoulos D (2018) Incremental training of deep convolutional neural networks. arXiv:1803.10232
Mishra P, Varadharajan V, Tupakula U, Pilli ES (2018) A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun Surv Tutor 21(1):686–728. https://doi.org/10.1109/COMST.2018.2847722
Moustafa N, Slay J, Creech G (2017) Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. IEEE Trans Big Data 5(4):481–494. https://doi.org/10.1109/TBDATA.2017.2715166
NG BA, Selvakumar S (2020) Anomaly detection framework for internet of things traffic using vector convolutional deep learning approach in fog environment. Future Gener Comput Syst 113, 255–265. https://doi.org/10.1016/j.future.2020.07.020
Pratama M, Ashfahani A, Ong YS, Ramasamy S, Lughofer E (2018) Autonomous deep learning: Incremental learning of denoising autoencoder for evolving data streams. arXiv:1809.09081
Shone N, Ngoc TN, Phai VD, Shi Q (2018) A deep learning approach to network intrusion detection. IEEE Trans Emerg Top Comput Intell 2(1):41–50. https://doi.org/10.1109/TETCI.2017.2772792
Yousefi-Azar M, Varadharajan V, Hamey L, Tupakula U (2017) Autoencoder-based feature learning for cyber security applications. In: 2017 International joint conference on neural networks (IJCNN). IEEE, pp 3854–3861
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhuvaneswari Amma, N.G. (2022). Network Traffic Classification Using Deep Autonomous Learning Approach. In: Rao, U.P., Patel, S.J., Raj, P., Visconti, A. (eds) Security, Privacy and Data Analytics. Lecture Notes in Electrical Engineering, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-16-9089-1_15
Download citation
DOI: https://doi.org/10.1007/978-981-16-9089-1_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9088-4
Online ISBN: 978-981-16-9089-1
eBook Packages: Computer ScienceComputer Science (R0)