Abstract
Intrusion detection is the key research direction of network security. With the rapid growth of network data and the enrichment of intrusion methods, traditional detection methods can no longer meet the security requirements of the current network environment. In recent years, the rapid development of deep learning technology and its great success in the field of imagery have provided a new solution for network intrusion detection. By visualizing the network data, this paper proposes an intrusion detection method based on deep learning and transfer learning, which transforms the intrusion detection problem into image recognition problem. Specifically, the stream data visualization method is used to present the network data in the form of a grayscale image, and then a deep learning method is introduced to detect the network intrusion according to the texture features in the grayscale image. Finally, transfer learning is introduced to improve the iterative efficiency and adaptability of the model. The experimental results show that the proposed method is more efficient and robust than the mainstream machine learning and deep learning methods, and has better generalization performance, which can detect new intrusion methods more effectively.
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Acknowledgment
This work was supported in part by the National Key R&D Program (No. 2018YFC0831006 and 2017YFB1400102), the Key Research and Development Plan of Shandong Province (No. 2017CXGC1503 and 2018GSF118228).
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Xu, Y. et al. (2020). Intrusion Detection Based on Fusing Deep Neural Networks and Transfer Learning. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_18
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DOI: https://doi.org/10.1007/978-981-15-3341-9_18
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