{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:24:44Z","timestamp":1743045884439,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030676605"},{"type":"electronic","value":"9783030676612"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-67661-2_5","type":"book-chapter","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T07:10:26Z","timestamp":1614150626000},"page":"69-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Activation Anomaly Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7901-7168","authenticated-orcid":false,"given":"Philip","family":"Sperl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1787-4102","authenticated-orcid":false,"given":"Jan-Philipp","family":"Schulze","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9337-7506","authenticated-orcid":false,"given":"Konstantin","family":"B\u00f6ttinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"5_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Andrews, J.T.A., Morton, E.J., Griffin, L.D.: Detecting anomalous data using auto-encoders. Int. J. Mach. Learn. Comput. 6(1), 21\u201326 (2016). https:\/\/doi.org\/10.18178\/ijmlc.2016.6.1.565","DOI":"10.18178\/ijmlc.2016.6.1.565"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Chawla, S.: Deep Learning for Anomaly Detection: A Survey. arXiv:1901.03407 (2019)","DOI":"10.1145\/3394486.3406704"},{"issue":"3","key":"5_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1\u201358 (2009). https:\/\/doi.org\/10.1145\/1541880.1541882","journal-title":"ACM Comput. Surv."},{"key":"5_CR5","unstructured":"Chollet, F.: Keras Documentation (2015). https:\/\/keras.io\/examples\/mnist_cnn\/"},{"key":"5_CR6","unstructured":"Chollet, F.: Building Autoencoders in Keras (2016). https:\/\/blog.keras.io\/building-autoencoders-in-keras.html"},{"key":"5_CR7","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2921\u20132926 (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7966217","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"5_CR9","doi-asserted-by":"publisher","unstructured":"Das, S., Wong, W.K., Dietterich, T., Fern, A., Emmott, A.: Incorporating expert feedback into active anomaly discovery. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 853\u2013858 (2017). https:\/\/doi.org\/10.1109\/icdm.2016.0102","DOI":"10.1109\/icdm.2016.0102"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Feng, C., Palleti, V.R., Mathur, A., Chana, D.: A systematic framework to generate invariants for anomaly detection in industrial control systems. In: Network and Distributed Systems Security (NDSS) Symposium 2019 (2019). https:\/\/doi.org\/10.14722\/ndss.2019.23265","DOI":"10.14722\/ndss.2019.23265"},{"key":"5_CR11","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672\u20132680. Curran Associates, Inc. (2014). http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf"},{"key":"5_CR12","unstructured":"Kingma, D.P., Ba, J.L.: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 (2014)"},{"key":"5_CR13","unstructured":"Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. arXiv:1312.6114 (2013)"},{"issue":"11","key":"5_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413\u2013422 (2008). https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Ma, L., et al.: DeepGauge: multi-granularity testing criteria for deep learning systems. In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, pp. 120\u2013131 (2018). https:\/\/doi.org\/10.1145\/3238147.3238202","DOI":"10.1145\/3238147.3238202"},{"key":"5_CR17","unstructured":"Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., Shroff, G.: LSTM-based encoder-decoder for multi-sensor anomaly detection. In: ICML 2016 Anomaly Detection Workshop (2016). http:\/\/arxiv.org\/abs\/1607.00148"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. In: Network and Distributed Systems Security (NDSS) Symposium 2018 (2018). https:\/\/doi.org\/10.14722\/ndss.2018.23204","DOI":"10.14722\/ndss.2018.23204"},{"key":"5_CR19","unstructured":"Nakae, T.: DAGMM TF Implementation. https:\/\/github.com\/tnakae\/DAGMM"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Nguyen, T.D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., Sadeghi, A.R.: DIoT: a federated self-learning anomaly detection system for IoT. In: Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS), Dallas, USA, July 2019. https:\/\/doi.org\/10.1109\/ICDCS.2019.00080","DOI":"10.1109\/ICDCS.2019.00080"},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Pang, G., Shen, C., van den Hengel, A.: Deep anomaly detection with deviation networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353\u2013362, November 2019. https:\/\/doi.org\/10.1145\/3292500.3330871","DOI":"10.1145\/3292500.3330871"},{"key":"5_CR22","unstructured":"Pang, G., Shen, C., Jin, H., van den Hengel, A.: Deep Weakly-supervised Anomaly Detection. arXiv:1910.13601, October 2019"},{"key":"5_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Pei, K., Cao, Y., Yang, J., Jana, S.: Deepxplore: automated whitebox testing of deep learning systems. In: Proceedings of the 26th Symposium on Operating Systems Principles, pp. 1\u201318 (2017). https:\/\/doi.org\/10.1145\/3132747.3132785","DOI":"10.1145\/3132747.3132785"},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Pozzolo, A.D., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 159\u2013166. IEEE (2015). https:\/\/doi.org\/10.1109\/SSCI.2015.33","DOI":"10.1109\/SSCI.2015.33"},{"issue":"8","key":"5_CR26","doi-asserted-by":"publisher","first-page":"3135","DOI":"10.1007\/s00521-019-04152-6","volume":"32","author":"AS Qureshi","year":"2019","unstructured":"Qureshi, A.S., Khan, A., Shamim, N., Durad, M.H.: Intrusion detection using deep sparse auto-encoder and self-taught learning. Neural Comput. Appl. 32(8), 3135\u20133147 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04152-6","journal-title":"Neural Comput. Appl."},{"key":"5_CR27","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. In: International Conference on Learning Representations (ICLR) (2020). https:\/\/openreview.net\/forum?id=HkgH0TEYwH"},{"key":"5_CR28","unstructured":"Sch\u00f6lkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Piatt, J.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, pp. 582\u2013588 (2000). https:\/\/papers.nips.cc\/paper\/1723-support-vector-method-for-novelty-detection.pdf"},{"key":"5_CR29","doi-asserted-by":"publisher","unstructured":"Schulze, J.P., Mrowca, A., Ren, E., Loeliger, H.A., B\u00f6ttinger, K.: Context by proxy: identifying contextual anomalies using an output proxy. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD 2019, pp. 2059\u20132068 (2019). https:\/\/doi.org\/10.1145\/3292500.3330780","DOI":"10.1145\/3292500.3330780"},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy, ICISSP 2018, pp. 108\u2013116 (2018). https:\/\/doi.org\/10.5220\/0006639801080116","DOI":"10.5220\/0006639801080116"},{"key":"5_CR31","doi-asserted-by":"publisher","unstructured":"Shekari, T., Bayens, C., Cohen, M., Graber, L., Beyah, R.: RFDIDS: radio frequency-based distributed intrusion detection system for the power grid. In: Network and Distributed Systems Security (NDSS) Symposium 2019 (2019). https:\/\/doi.org\/10.14722\/ndss.2019.23462","DOI":"10.14722\/ndss.2019.23462"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Sperl, P., Kao, C.Y., Chen, P., Lei, X., B\u00f6ttinger, K.: DLA: dense-layer-analysis for adversarial example detection. In: 5th IEEE European Symposium on Security and Privacy (2020). http:\/\/arxiv.org\/abs\/1911.01921","DOI":"10.1109\/EuroSP48549.2020.00021"},{"key":"5_CR33","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR34","doi-asserted-by":"publisher","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136 (2009). https:\/\/doi.org\/10.1109\/CISDA.2009.5356528","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"5_CR35","doi-asserted-by":"publisher","unstructured":"Veeramachaneni, K., Arnaldo, I., Korrapati, V., Bassias, C., Li, K.: AI$$^2$$: training a big data machine to defend. In: 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), pp. 49\u201354 (2016). https:\/\/doi.org\/10.1109\/BigDataSecurity-HPSC-IDS.2016.79","DOI":"10.1109\/BigDataSecurity-HPSC-IDS.2016.79"},{"key":"5_CR36","doi-asserted-by":"publisher","unstructured":"Yousefi-Azar, M., Varadharajan, V., Hamey, L., Tupakula, U.: Autoencoder-based feature learning for cyber security applications. In: Proceedings of the International Joint Conference on Neural Networks, pp. 3854\u20133861 (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7966342","DOI":"10.1109\/IJCNN.2017.7966342"},{"key":"5_CR37","doi-asserted-by":"publisher","unstructured":"Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.R.: Adversarially learned anomaly detection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 727\u2013736 (2018). https:\/\/doi.org\/10.1109\/ICDM.2018.00088","DOI":"10.1109\/ICDM.2018.00088"},{"key":"5_CR38","doi-asserted-by":"publisher","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 (2017). https:\/\/doi.org\/10.1145\/3097983.3098052","DOI":"10.1145\/3097983.3098052"},{"key":"5_CR39","unstructured":"Zhou, Z., Zhou, W., Lv, X., Huang, X., Wang, X., Li, H.: Progressive Learning of Low-Precision Networks. arXiv:1905.11781 (2019)"},{"key":"5_CR40","unstructured":"Zong, B., et al.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=BJJLHbb0-"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67661-2_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T23:02:17Z","timestamp":1740351737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67661-2_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030676605","9783030676612"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67661-2_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd2020.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"945","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"195","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4,4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}