Abstract
Network security has always been facing new challenges. Accurate and convenient detection of intrusions is needed to protect system security. When the system encounters an intrusion, there may be a problem of insufficient early samples for this type of attack, resulting in a low recognition rate. It is necessary to consider whether it can be combined with a suitable intrusion detection method to realize the detection of abnormal data with only a small number of samples. In this paper, we propose an intrusion detection method based on small sample learning, which can process the intrusion behavior information so as to realize the classification of abnormal behaviors when the previous similar samples are insufficient. And ResNet is selected as the classification model to build a deeper network. We gradually increased the number of iterations and the number of small samples in the experiment, and got the performance changes of different models. Compared with CNN, SVM and other algorithms, the intrusion detection method is evaluated by performance indicators such as accuracy rate and false alarm rate. It is finally proved that ResNet can better deal with the intrusion detection classification problem under small sample data. It is more feasible and accurate, and can be widely used to determine network intrusion behavior.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
François, D., Wertz, V., Verleysen, M.: Choosing the metric: a simple model approach. In: Jankowski, N., Duch, W., Gra̧bczewski, K. (eds.) Meta-Learning in Computational Intelligence, vol. 358, pp. 97–115. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20980-2_3
Lake, B., Salakhutdinov, R.: One-shot learning by inverting a compositional causal process. Adv. Neural. Inf. Process. Syst. 26, 2526–2534 (2015)
Li, X.Y., Long, S.P., Zhu, J.: Survey of few-shot learning based on deep neural network. Appl. Res. Comput. 37(8), 2241–2247 (2020)
Anderson, J.P.: Computer security threat monitoring and surveillance. James P. Anderson Co, Fort, pp. 523–544 (1980)
Denning, D.E.: An Intrusion-Detection Model, pp. 879–888. IEEE Press (1987)
Li, Z., Zhou, F., Chen, F., et al.: Meta-SGD: learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835, pp. 322–433 (2017)
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: 5th International Conference on Learning Representations, pp. 222–283. ICLR, Toulon (2017)
Li, Y., Xu, Y., Liu, Z., et al.: Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement 154, 107–150 (2019)
Zhang, J., Ling, Y., Fu, X., et al.: Model of the intrusion detection system based on the integration of spatial-temporal features. Comput. Secur. 89, 101–281 (2020)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126–1135. PMLR, Sydney (2017)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: 32nd International Conference on Machine Learning, vol. 2, pp. 201–332. IMLS, Lile (2015)
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D.: Matching networks for one shot learning. In: Proceedings of the 30th Conference on Neural Information Processing Systems, pp. 3630–3638. Neural Information Processing Systems Foundation, Barcelona (2016)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Proceedings of the 31st Conference on Neural Information Processing Systems, vol. 2017-December, pp. 4077–4087. Neural Information Processing Systems Foundation, Long Beach (2017)
Wang, Y.X., Hebert, M.: Learning from small sample sets by combining unsupervised meta-training with CNNs. Advances in Neural Information Processing Systems 29-Proceedings of the 2016 Conference, vol. 0, 244–252. Neural Information Processing Systems Foundation, Barcelona (2016)
Boney, R., Ilin, A.: Semi-supervised few-shot learning with MAML. In: 6th International Conference on Learning Representations, ICLR 2018-Workshop Track Proceedings, 22–32, International Conference on Learning Representations, Vancouver (2018)
Ren, M.Y., Triantafillou, E., Ravi, S., et al.: Meta-learning for semi-supervised few-shot classification. arXiv preprint arXiv:1803.00676, pp. 333–344 (2018)
Liu, Y., Lee, J., Park, M., et al.: Learning to propagate labels: transductive propagation network for few-shot learning. arXiv preprint arXiv:1805.10002, pp. 888–1089 (2018)
Hou, R.B., Chang, H., Ma, B.P., et al.: Cross attention network for few-shot classification. In: 33rd Annual Conference on Neural Information Processing Systems, vol. 32, pp. 4003–4014. Neural Information Processing Systems Foundation, Vancouver (2019)
Chu, B., Madhavan, V., Beijbom, O., Hoffman, J., Darrell, T.: Best practices for fine-tuning visual classifiers to new domains. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 435–442. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_34
Acknowledgement
This work was supported by the Key Research and Development Program of Hainan Province (Grant No. ZDYF2020040), Major science and technology project of Hainan Province (Grant No. ZDKJ2020012), Hainan Provincial Natural Science Foundation of China (Grant Nos. 2019RC098) and National Natural Science Foundation of China (NSFC) (Grant No. 62162022, 62162024 and 61762033).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, H. et al. (2021). Intrusion Detection Method Based on Small Sample Learning. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_15
Download citation
DOI: https://doi.org/10.1007/978-981-16-7502-7_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7501-0
Online ISBN: 978-981-16-7502-7
eBook Packages: Computer ScienceComputer Science (R0)