{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T21:43:16Z","timestamp":1760305396150,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032080660"},{"type":"electronic","value":"9783032080677"}],"license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-08067-7_15","type":"book-chapter","created":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T20:37:31Z","timestamp":1760301451000},"page":"294-312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-supervised Time-Series Anomaly Detection with\u00a0Temporal Logic Explanations"],"prefix":"10.1007","author":[{"given":"Mahshid","family":"Noorani","sequence":"first","affiliation":[]},{"given":"Aniruddh G.","family":"Puranic","sequence":"additional","affiliation":[]},{"given":"Jack","family":"Mirenzi","sequence":"additional","affiliation":[]},{"given":"John S.","family":"Baras","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","unstructured":"Akbarian, H., Mahgoub, I., Williams, A.: Autoencoder-LSTM algorithm for anomaly detection. In: 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET), pp.\u00a01\u20136 (2023). https:\/\/doi.org\/10.1109\/HONET59747.2023.10374710","DOI":"10.1109\/HONET59747.2023.10374710"},{"key":"15_CR2","unstructured":"Box, G., Jenkins, G., Reinsel, G., Ljung, G.: Time series analysis: forecasting and control. Wiley Series in Probability and Statistics, Wiley (2015)"},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"47072","DOI":"10.1109\/ACCESS.2020.2977892","volume":"8","author":"T Chen","year":"2020","unstructured":"Chen, T., Liu, X., Xia, B., Wang, W., Lai, Y.: Unsupervised anomaly detection of industrial robots using sliding-window convolutional variational autoencoder. IEEE Access 8, 47072\u201347081 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2977892","journal-title":"IEEE Access"},{"issue":"1","key":"15_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10703-017-0286-7","volume":"51","author":"JV Deshmukh","year":"2017","unstructured":"Deshmukh, J.V., Donz\u00e9, A., Ghosh, S., Jin, X., Juniwal, G., Seshia, S.A.: Robust online monitoring of signal temporal logic. Formal Methods Syst. Des. 51(1), 5\u201330 (2017)","journal-title":"Formal Methods Syst. Des."},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-3-642-39799-8_19","volume-title":"Computer Aided Verification","author":"A Donz\u00e9","year":"2013","unstructured":"Donz\u00e9, A., Ferr\u00e8re, T., Maler, O.: Efficient robust monitoring for STL. In: Sharygina, N., Veith, H. (eds.) Computer Aided Verification, pp. 264\u2013279. Springer, Berlin Heidelberg, Berlin, Heidelberg (2013)"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Donz\u00e9, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: FORMATS (2010)","DOI":"10.1007\/978-3-642-15297-9_9"},{"issue":"45","key":"15_CR7","first-page":"1","volume":"17","author":"\u00dc Do\u011fan","year":"2016","unstructured":"Do\u011fan, \u00dc., Glasmachers, T., Igel, C.: A unified view on multi-class support vector classification. J. Mach. Learn. Res. 17(45), 1\u201332 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Fainekos, G.E., Pappas, G.J.: Robustness of temporal logic specifications for continuous-time signals. Theor. Comput. Sci. (2009)","DOI":"10.1016\/j.tcs.2009.06.021"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Ferfoglia, I., Saveri, G., Nenzi, L., Bortolussi, L.: Ecats: Explainable-by-design concept-based anomaly detection for time series. In: Besold, T.R., d\u2019Avila Garcez, A., Jimenez-Ruiz, E., Confalonieri, R., Madhyastha, P., Wagner, B. (eds.) Neural-Symbolic Learning and Reasoning, pp. 175\u2013191. Springer Nature Switzerland, Cham (2024)","DOI":"10.1007\/978-3-031-71170-1_16"},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"Graab\u00e6k, S.G., et\u00a0al.: An experimental comparison of anomaly detection methods for collaborative robot manipulators. TechRxiv (2023). https:\/\/doi.org\/10.36227\/techrxiv.19006643.v3","DOI":"10.36227\/techrxiv.19006643.v3"},{"issue":"3","key":"15_CR11","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/s10703-019-00332-1","volume":"54","author":"S Jha","year":"2019","unstructured":"Jha, S., Tiwari, A., Seshia, S.A., Sahai, T., Shankar, N.: Telex: learning signal temporal logic from positive examples using tightness metric. Formal Methods Syst. Des. 54(3), 364\u2013387 (2019)","journal-title":"Formal Methods Syst. Des."},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Kong, Z., Jones, A., Medina\u00a0Ayala, A., Aydin\u00a0Gol, E., Belta, C.: Temporal logic inference for classification and prediction from data. In: Proceedings of the 17th International Conference on Hybrid Systems: Computation and Control, pp. 273\u2013282. HSCC \u201914, Association for Computing Machinery, New York, NY, USA (2014). https:\/\/doi.org\/10.1145\/2562059.2562146","DOI":"10.1145\/2562059.2562146"},{"key":"15_CR13","doi-asserted-by":"publisher","unstructured":"Li, D., Cai, M., Vasile, C.I., Tron, R.: Learning signal temporal logic through neural network for interpretable classification. In: 2023 American Control Conference (ACC), pp. 1907\u20131914 (2023). https:\/\/doi.org\/10.23919\/ACC55779.2023.10156357","DOI":"10.23919\/ACC55779.2023.10156357"},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Li, Z., Zhu, Y., Van\u00a0Leeuwen, M.: A survey on explainable anomaly detection. ACM Trans. Knowl. Discov. Data 18(1) (2023). https:\/\/doi.org\/10.1145\/3609333","DOI":"10.1145\/3609333"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: Lakhnech, Y., Yovine, S. (eds.) Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems, pp. 152\u2013166. Springer, Berlin Heidelberg, Berlin, Heidelberg (2004)","DOI":"10.1007\/978-3-540-30206-3_12"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Meli, D.: Explainable online unsupervised anomaly detection for cyber-physical systems via causal discovery from time series (2024)","DOI":"10.1109\/CASE59546.2024.10711445"},{"key":"15_CR17","doi-asserted-by":"publisher","unstructured":"Mohammadinejad, S., Deshmukh, J.V., Puranic, A.G., Vazquez-Chanlatte, M., Donz\u00e9, A.: Interpretable classification of time-series data using efficient enumerative techniques. In: Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control. HSCC \u201920, Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3365365.3382218","DOI":"10.1145\/3365365.3382218"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Noorani, M., Puthanveettil, T.V., Zoulkarni, A., Mirenzi, J., Grody, C.D., Baras, J.S.: Multimodal anomaly detection for autonomous cyber-physical systems empowering real-world evaluation. In: Sinha, A., Fu, J., Zhu, Q., Zhang, T. (eds.) Decision and Game Theory for Security, pp. 306\u2013325. Springer Nature Switzerland, Cham (2025)","DOI":"10.1007\/978-3-031-74835-6_15"},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":"Zhai, J., Zhang, S., Chen, J., He, Q.: Autoencoder and its various variants. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 415\u2013419 (2018). https:\/\/doi.org\/10.1109\/SMC.2018.00080","DOI":"10.1109\/SMC.2018.00080"},{"issue":"2","key":"15_CR20","doi-asserted-by":"publisher","first-page":"2728","DOI":"10.1109\/JIOT.2023.3293860","volume":"11","author":"H Zhu","year":"2024","unstructured":"Zhu, H., Yi, C., Rho, S., Liu, S., Jiang, F.: An interpretable multivariate time-series anomaly detection method in cyber\u2013physical systems based on adaptive mask. IEEE Internet Things J. 11(2), 2728\u20132740 (2024). https:\/\/doi.org\/10.1109\/JIOT.2023.3293860","journal-title":"IEEE Internet Things J."}],"container-title":["Lecture Notes in Computer Science","Game Theory and AI for Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08067-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T21:02:51Z","timestamp":1760302971000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08067-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"ISBN":["9783032080660","9783032080677"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08067-7_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,13]]},"assertion":[{"value":"13 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GameSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Game Theory and AI for Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gamesec2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.gamesec-conf.org\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}