Abstract
Anomaly detection aims to distinguish significant deviation data from an observed dataset, which has wide applications in various fields. Autoencoder (AE) is an effective approach, which maps the original data into latent feature space, and then identifies the anomalies with higher reconstruction errors. However, the performance of autoencoder-based approach heavily relies on feature representations in the latent space, which requires the feature representations be captured as much essential as possible. Therefore, a graph regularization constraint term is first introduced into Deep Autoencoder (DAE) to explore the geometric structure information. Moreover, to avoid the problem of overfitting and enhance the ability of feature representations, a constraint term is imposed and then a Deep Sparse Graph Regularized Autoencoder (DSGRAE) approach is proposed. Finally, we carry out extensive experiments on 14 widely used datasets and compare them with other state-of-the-art methods, which demonstrate the effectiveness of the proposed method.
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References
Xie, X., Wang, C., Chen, S., et al.: Real-time illegal parking detection system based on deep learning. In: Proceedings of the 2017 International Conference on Deep Learning Technologies, pp. 23–27 (2017)
Kaur, M., Kamra, A.: Detection of retinal abnormalities in fundus image using transfer learning networks. Soft Computing 1-15 (2021)
Shone, N., Ngoc, T.N., Phai, V.D., et al.: A deep learning approach to network intrusion detection. IEEE Trans. Emerging Topics Computational Intelligence 2(1), 41–50 (2018)
Pan, K., Palensky, P., Esfahani, P.M.: From static to dynamic anomaly detection with application to power system cyber security. IEEE Trans. Power Syst. 35(2), 1584–1596 (2019)
Venkataramanaiah, B., Kamala, J.: ECG signal processing and KNN classifier-based abnormality detection by VH-doctor for remote cardiac healthcare monitoring. Soft. Comput. 24(22), 17457–17466 (2020)
Pourhabibi, T., Ong, K.L., Kam, B.H., et al.: Fraud detection: a systematic literature review of graph-based anomaly detection approaches. Decis. Support Syst. 133, 113303 (2020)
Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. 53(3), 1–37 (2020)
Pang, G., Shen, C., Cao, L., et al.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2), 1–38 (2021)
Chalapathy, R., Chawla, S.: Deep Learning for Anomaly Detection: A Survey. arXiv preprint arXiv:1901.03407 (2019)
Lu, W., Cheng, Y., Xiao, C., et al.: Unsupervised sequential outlier detection with deep architectures. IEEE Trans. Image Process. 26(9), 4321–4330 (2017)
Hyperspectral image classification using k-sparse denoising autoencoder and spectral–restricted spatial characteristics. Applied Soft Computing 74, 693–708 (2019)
Chen, J., Sathe, S., Aggarwal, C., et al.: Outlier detection with autoencoder ensembles. In: Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp. 90–98 (2017)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 665–674 (2017)
Xu, D., Ricci, E., Yan, Y., et al.: Learning Deep Representations of Appearance and Motion for Anomalous Event Detection. arXiv preprint arXiv: 1510.01553 (2015)
Hasan, M., Choi, J., Neumann, J., et al.: Learning temporal regularity in video sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–742 (2016)
Zhang, C., Song, D., Chen, Y., et al.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. Proceedings of the AAAI Conference on Artificial Intelligence 33(01), 1409–1416 (2019)
Malhotra, P., Ramakrishnan, A., Anand, G., et al.: LSTM-Based Encoder-Decoder for Multi-Sensor Anomaly Detection. arXiv preprint arXiv: 1607.00148 (2016)
Luo, W., Liu, W., Gao, S.: Remembering history with convolutional LSTM for anomaly detection. In: 2017 IEEE International Conference on Multimedia and Expo, pp. 439-444 (2017)
Liao, Y., Wang, Y., Liu, Y.: Graph regularized auto-encoders for image representation. IEEE Trans. Image Process. 26(6), 2839–2852 (2016)
Zhai, J., Zhang, S., Chen, J., et al.: Autoencoder and its various variants. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, pp. 415–419 (2018)
Campos, G.O., et al.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Disc. 30(4), 891–927 (2016)
Acknowledgment
This work is supported in part by grants from the National Natural Science Foundation of China (No. 62062040), the Outstanding Youth Project of Jiangxi Natural Science Foundation (No. 20212ACB212003), the Jiangxi Province Key Subject Academic and Technical Leader Funding Project (No. 20212BCJ23017).
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Li, S., Yang, X., Zhang, H., Zheng, C., Yi, Y. (2023). DSGRAE: Deep Sparse Graph Regularized Autoencoder for Anomaly Detection. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_21
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