State of the Art Literature Review on Network Anomaly Detection with Deep Learning

  • Tero BodströmEmail author
  • Timo Hämäläinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11118)


As network attacks are evolving along with extreme growth in the amount of data that is present in networks, there is a significant need for faster and more effective anomaly detection methods. Even though current systems perform well when identifying known attacks, previously unknown attacks are still difficult to identify under occurrence. To emphasize, attacks that might have more than one ongoing attack vectors in one network at the same time, or also known as APT (Advanced Persistent Threat) attack, may be hardly notable since it masquerades itself as legitimate traffic. Furthermore, with the help of hiding functionality, this type of attack can even hide in a network for years. Additionally, the expected number of connected devices as well as the fast-paced development caused by the Internet of Things, raises huge risks in cyber security that must be dealt with accordingly. When considering all above-mentioned reasons, there is no doubt that there is plenty of room for more advanced methods in network anomaly detection hence Deep Learning based techniques have been proposed recently in detecting anomalies.

The papers reviewed showed that different Deep Learning methods vary in their performance to detect anomalies, but neural networks capability to adapt to rapidly changing network environments by self learning, to handle multi-dimensional data and to detect previously unknown attacks gives a huge advantage for detecting sophisticated attacks such as APT.


Network attacks Anomaly detection Deep learning 


  1. 1.
    Andropov, S., Guirik, A., Budko, M., Budko, M.: Network anomaly detection using artificial neural networks. In: 2017 20th Conference of Open Innovations Association (FRUCT) (2017).
  2. 2.
    Aygun, R.C., Yavuz, A.G.: Network anomaly detection with stochastically improved autoencoder based models. In: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing, pp. 193–198 (2017).
  3. 3.
    Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017). Scholar
  4. 4.
    Thing, V.L.L.: IEEE 802.11 network anomaly detection and attack classification: a deep learning approach. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC) (2017).
  5. 5.
    Van, N., Thinh, T., Sach, L.: An anomaly-based network intrusion detection system using deep learning. In: 2017 International Conference on System Science and Engineering (ICSSE), pp. 210–214.
  6. 6.
    Baek, S., Kwon, D., Kim, J., Suh, S.C., Kim, H., Kim, I.: Unsupervised labeling for supervised anomaly detection in enterprise and cloud networks. In: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing, pp. 205–210 (2017).
  7. 7.
    Yuan, X., Li, C., Li, X.: DeepDefense: identifying DDoS attack via deep learning. In: 2017 IEEE International Conference on Smart Computing (SMARTCOMP) (2017).
  8. 8.
    Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: 2017 International Conference on Information Networking (ICOIN), pp. 712–717 (2017).
  9. 9.
    He, Y., Mendis, G.J., Wei, J.: Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism. IEEE Trans. Smart Grid, 2505–2516 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Information TechnologyUniversity of JyväskyläJyväskyläFinland

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