DAFL: Deep Adaptive Feature Learning for Network Anomaly Detection

  • Shujian Ji
  • Tongzheng Sun
  • Kejiang YeEmail author
  • Wenbo Wang
  • Cheng-Zhong Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)


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.


Network anomaly detection Deep learning Feature learning 



This work is supported by the National Key R&D Program of China (No. 2018YFB1004804), National Natural Science Foundation of China (No. 61702492), Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence, and Shenzhen Basic Research Program (No. JCYJ20170818153016513).


  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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). Scholar
  5. 5.
    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). Scholar
  6. 6.
    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). Scholar
  7. 7.
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 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)Google Scholar
  11. 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)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Shujian Ji
    • 1
    • 2
  • Tongzheng Sun
    • 1
  • Kejiang Ye
    • 1
    Email author
  • Wenbo Wang
    • 3
  • Cheng-Zhong Xu
    • 4
  1. 1.Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Khoury College of Computer SciencesNortheastern UniversitySeattleUSA
  4. 4.Faculty of Science and TechnologyUniversity of MacauMacauChina

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