Abstract
In network intrusion detection, the recognition and detection rate of intrusion detection may be reduced due to the lack of sample attributes or insufficient labels. In order to overcome this problem, this paper proposes a semi-supervised intrusion detection algorithm based on auto-encoder. The auto-encoder is adopted to extract features from all samples. For the labeled samples, cross-entropy is adopted for classification. For the unlabeled samples, first, initialize K (number of categories) category centers in the feature space based on the characteristics of partially labeled samples. Constrain the representation of unlabeled samples at the center of a certain category, and then place the restricted representation into the classifier for classification. By combining data from labeled samples and unlabeled samples, and simultaneously updating network parameters and category centers, semi-supervised intrusion detection is achieved. The proposed algorithm is verified on NSL-KDD and KDD CUP99 datasets. Experimental results show that the method can not only effectively reduce the dependence on labeled samples, but also improve the accuracy of intrusion detection to a certain extent.
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Acknowledgments
This work was supported in part by the National Nature Science Foundation of China (Grant No. 61471182), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX20_2993), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 15KJD52004).
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Du, X., Li, Y., Feng, Z. (2021). A Semi-supervised Intrusion Detection Algorithm Based on Auto-encoder. In: Wang, G., Chen, B., Li, W., Di Pietro, R., Yan, X., Han, H. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2020. Lecture Notes in Computer Science(), vol 12382. Springer, Cham. https://doi.org/10.1007/978-3-030-68851-6_13
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