{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:58:57Z","timestamp":1743091137979,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200984"},{"type":"electronic","value":"9783031200991"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-20099-1_21","type":"book-chapter","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:04:11Z","timestamp":1673535851000},"page":"254-265","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DSGRAE: Deep Sparse Graph Regularized Autoencoder for Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Shicheng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugen","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"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\u201327 (2017)","DOI":"10.1145\/3094243.3094261"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Kaur, M., Kamra, A.: Detection of retinal abnormalities in fundus image using transfer learning networks. Soft Computing 1-15 (2021)","DOI":"10.1007\/s00500-021-06088-3"},{"issue":"1","key":"21_CR3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"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\u201350 (2018)","journal-title":"IEEE Trans. Emerging Topics Computational Intelligence"},{"issue":"2","key":"21_CR4","doi-asserted-by":"publisher","first-page":"1584","DOI":"10.1109\/TPWRS.2019.2943304","volume":"35","author":"K Pan","year":"2019","unstructured":"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\u20131596 (2019)","journal-title":"IEEE Trans. Power Syst."},{"issue":"22","key":"21_CR5","doi-asserted-by":"publisher","first-page":"17457","DOI":"10.1007\/s00500-020-05191-1","volume":"24","author":"B Venkataramanaiah","year":"2020","unstructured":"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\u201317466 (2020)","journal-title":"Soft. Comput."},{"key":"21_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113303","volume":"133","author":"T Pourhabibi","year":"2020","unstructured":"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)","journal-title":"Decis. Support Syst."},{"issue":"3","key":"21_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381028","volume":"53","author":"A Boukerche","year":"2020","unstructured":"Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. 53(3), 1\u201337 (2020)","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"21_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang, G., Shen, C., Cao, L., et al.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv."},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Chawla, S.: Deep Learning for Anomaly Detection: A Survey. arXiv preprint arXiv:1901.03407 (2019)","DOI":"10.1145\/3394486.3406704"},{"issue":"9","key":"21_CR10","doi-asserted-by":"publisher","first-page":"4321","DOI":"10.1109\/TIP.2017.2713048","volume":"26","author":"W Lu","year":"2017","unstructured":"Lu, W., Cheng, Y., Xiao, C., et al.: Unsupervised sequential outlier detection with deep architectures. IEEE Trans. Image Process. 26(9), 4321\u20134330 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Hyperspectral image classification using k-sparse denoising autoencoder and spectral\u2013restricted spatial characteristics. Applied Soft Computing 74, 693\u2013708 (2019)","DOI":"10.1016\/j.asoc.2018.08.049"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"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\u201398 (2017)","DOI":"10.1137\/1.9781611974973.11"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"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\u2013674 (2017)","DOI":"10.1145\/3097983.3098052"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.5244\/C.29.8"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"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\u2013742 (2016)","DOI":"10.1109\/CVPR.2016.86"},{"issue":"01","key":"21_CR16","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1609\/aaai.v33i01.33011409","volume":"33","author":"C Zhang","year":"2019","unstructured":"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\u20131416 (2019)","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"21_CR17","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., et al.: LSTM-Based Encoder-Decoder for Multi-Sensor Anomaly Detection. arXiv preprint arXiv: 1607.00148 (2016)"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1109\/ICME.2017.8019325"},{"issue":"6","key":"21_CR19","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.1109\/TIP.2016.2605010","volume":"26","author":"Y Liao","year":"2016","unstructured":"Liao, Y., Wang, Y., Liu, Y.: Graph regularized auto-encoders for image representation. IEEE Trans. Image Process. 26(6), 2839\u20132852 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"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\u2013419 (2018)","DOI":"10.1109\/SMC.2018.00080"},{"issue":"4","key":"21_CR21","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","volume":"30","author":"GO Campos","year":"2016","unstructured":"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\u2013927 (2016)","journal-title":"Data Min. Knowl. Disc."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20099-1_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:21:10Z","timestamp":1673536870000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20099-1_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031200984","9783031200991"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20099-1_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}