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2019 | OriginalPaper | Chapter

DAFL: Deep Adaptive Feature Learning for Network Anomaly Detection

Authors : Shujian Ji, Tongzheng Sun, Kejiang Ye, Wenbo Wang, Cheng-Zhong Xu

Published in: Network and Parallel Computing

Publisher: Springer International Publishing

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Abstract

With the rapid development of the Internet and the growing complexity of the network topology, network anomaly has become more diverse. In this paper, we propose an algorithm named Deep Adaptive Feature Learning (DAFL) for traffic anomaly detection based on deep learning model. By setting proper feature parameters \(\theta \) on the neural network structure, DAFL can effectively generate low-dimensional new abstract features. Experimental results show the DAFL algorithm has good adaptability and robustness, which can effectively improve the detection accuracy and significantly reduce the detection time.

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Lin, P., Ye, K., Xu, C.-Z.: NetDetector: an anomaly detection platform for networked systems. In: IEEE International Conference on Real-time Computing and Robotics. IEEE (2019)

Shon, T., Kim, Y., Lee, C., Moon, J.: A machine learning framework for network anomaly detection using SVM and GA. In: Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop, pp. 176–183. IEEE (2005)

Amor, N.B., Benferhat, S., Elouedi, Z.: Naive Bayes vs decision trees in intrusion detection systems. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 420–424. ACM (2004)

Lin, P., Ye, K., Xu, C.-Z.: Dynamic network anomaly detection system by using deep learning techniques. In: Da Silva, D., Wang, Q., Zhang, L.J. (eds.) CLOUD 2019. LNCS, vol. 11513, pp. 161–176. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23502-4_12CrossRef

Zhu, M., Ye, K., Wang, Y., Xu, C.-Z.: A deep learning approach for network anomaly detection based on AMF-LSTM. In: Zhang, F., Zhai, J., Snir, M., Jin, H., Kasahara, H., Valero, M. (eds.) NPC 2018. LNCS, vol. 11276, pp. 137–141. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05677-3_13CrossRef

Zhu, M., Ye, K., Xu, C.-Z.: Network anomaly detection and identification based on deep learning methods. In: Luo, M., Zhang, L.-J. (eds.) CLOUD 2018. LNCS, vol. 10967, pp. 219–234. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94295-7_15CrossRef

NSL-KDD (1999). https://iscxdownloads.cs.unb.ca/iscxdownloads/NSL-KDD

Ibrahimi, K., Ouaddane, M.: Management of intrusion detection systems based-KDD99: analysis with LDA and PCA. In: 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 1–6. IEEE (2017)

Alrawashdeh, K., Purdy, C.: Toward an online anomaly intrusion detection system based on deep learning. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 195–200. IEEE (2016)

10.

Potluri, S., Diedrich, C.: Accelerated deep neural networks for enhanced intrusion detection system. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2016)

11.

Kang, M.-J., Kang, J.-W.: Intrusion detection system using deep neural network for in-vehicle network security. PloS one 11(6), e0155781 (2016)CrossRef

Title: DAFL: Deep Adaptive Feature Learning for Network Anomaly Detection
Authors: Shujian Ji
Tongzheng Sun
Kejiang Ye
Wenbo Wang
Cheng-Zhong Xu
Publisher: Springer International Publishing
Book: Network and Parallel Computing
Print ISBN: 978-3-030-30708-0

Electronic ISBN: 978-3-030-30709-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30709-7_32

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