Abstract
Anomaly detection in network traffic with the high-volume and rapidity is a challenging. Data arrives along with the time with latent distribution changes lead to a single stationary model that doesn’t match streaming data all the time. Therefore, it is necessary to maintain a dynamic system to adapt to changes in the network environment. In solving that problem, supervised learning methods have attracted extensive attention for their capability to detect attacks known as anomalies. However, they require a large amount of labeled training data to train an effective model, which is difficult and expensive to obtain. In this paper, we propose to use LSTM Auto-encoder to extract temporal features from different sequences of network packets that are unlabeled or partially labeled data. After obtaining good data representation, the feedforward neural networks are applied to perform the identification of network traffic anomalies. Our experimental results with the ISCX-IDS-2012 dataset show that our work obtains high performance in intrusion detection with accuracy approximately 99%.
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Van, N.T., Sach, L.T., Thinh, T.N. (2021). Temporal Features Learning Using Autoencoder for Anomaly Detection in Network Traffic. In: Huang, YP., Wang, WJ., Quoc, H.A., Giang, L.H., Hung, NL. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2020. Advances in Intelligent Systems and Computing, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-62324-1_2
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