{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:09:04Z","timestamp":1778458144475,"version":"3.51.4"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032200259","type":"print"},{"value":"9783032200266","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-20026-6_12","type":"book-chapter","created":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T23:43:47Z","timestamp":1778456627000},"page":"208-220","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Driven Energy Auditing for\u00a0Smart Grid Cyberattack Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7351-7120","authenticated-orcid":false,"given":"Alexandre","family":"Dohin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8485-8455","authenticated-orcid":false,"given":"Karim","family":"Zkik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4670-8982","authenticated-orcid":false,"given":"Mawloud","family":"Omar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4463-3048","authenticated-orcid":false,"given":"Abdellah","family":"Akilal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"issue":"1","key":"12_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-023-00200-w","volume":"7","author":"B Achaal","year":"2024","unstructured":"Achaal, B., Adda, M., Berger, M., Ibrahim, H., Awde, A.: Study of smart grid cyber-security, examining architectures, communication networks, cyber-attacks, countermeasure techniques, and challenges. Cybersecurity 7(1), 1\u201330 (2024). https:\/\/doi.org\/10.1186\/s42400-023-00200-w","journal-title":"Cybersecurity"},{"key":"12_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112614","volume":"305","author":"B Ahmad","year":"2024","unstructured":"Ahmad, B., Wu, Z., Huang, Y., et al.: Enhancing the security in IoT and IIoT networks: an intrusion detection scheme leveraging deep transfer learning. Knowl.-Based Syst. 305, 112614 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.112614","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"12_CR3","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1109\/TII.2022.3205366","volume":"19","author":"I Ahmed","year":"2022","unstructured":"Ahmed, I., Anisetti, M., Ahmad, A., Jeon, G.: A multilayer deep learning approach for malware classification in 5G-enabled IIoT. IEEE Trans. Industr. Inf. 19(2), 1495\u20131503 (2022). https:\/\/doi.org\/10.1109\/TII.2022.3205366","journal-title":"IEEE Trans. Industr. Inf."},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Alanazi, F., Kim, J., Cotilla-Sanchez, E.: Load oscillating attacks of smart grids: vulnerability analysis. IEEE Access 36538\u201336549 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3266249","DOI":"10.1109\/ACCESS.2023.3266249"},{"issue":"4","key":"12_CR5","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s40572-020-00295-0","volume":"7","author":"JA Casey","year":"2020","unstructured":"Casey, J.A., Fukurai, M., Hern\u00e1ndez, D., Balsari, S., Kiang, M.V.: Power outages and community health: a narrative review. Curr. Environ. Health Rep. 7(4), 371\u2013383 (2020). https:\/\/doi.org\/10.1007\/s40572-020-00295-0","journal-title":"Curr. Environ. Health Rep."},{"key":"12_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102808","volume":"170","author":"L Cui","year":"2020","unstructured":"Cui, L., Qu, Y., Gao, L., Xie, G., Yu, S.: Detecting false data attacks using machine learning techniques in smart grid: a survey. J. Netw. Comput. Appl. 170, 102808 (2020). https:\/\/doi.org\/10.1016\/j.jnca.2020.102808","journal-title":"J. Netw. Comput. Appl."},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Du, J., Yang, K., Hu, Y., et\u00a0al.: NIDS-CNNLSTM: network intrusion detection classification model based on deep learning. IEEE Access 24808\u201324821 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3254915","DOI":"10.1109\/ACCESS.2023.3254915"},{"issue":"4","key":"12_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1306","volume":"9","author":"A Handa","year":"2019","unstructured":"Handa, A., Sharma, A., Shukla, S.K.: Machine learning in cybersecurity: a review. WIREs Data Min. Knowl. Discov. 9(4), e1306 (2019). https:\/\/doi.org\/10.1002\/widm.1306","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Haq, F.U., Bhui, P., Chakravarthi, K.: Real time congestion management using plug in electric vehicles (PEV\u2019s): a game theoretic approach. IEEE Access 42029\u201342043 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3167847","DOI":"10.1109\/ACCESS.2022.3167847"},{"issue":"6","key":"12_CR10","doi-asserted-by":"publisher","first-page":"5724","DOI":"10.1109\/TKDE.2022.3157472","volume":"35","author":"MU Hassan","year":"2022","unstructured":"Hassan, M.U., Rehmani, M.H., Du, J.T., Chen, J.: Differentially private demand side management for incentivized dynamic pricing in smart grid. IEEE Trans. Knowl. Data Eng. 35(6), 5724\u20135737 (2022). https:\/\/doi.org\/10.1109\/TKDE.2022.3157472","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"12_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.104135","volume":"148","author":"D He","year":"2025","unstructured":"He, D., Wang, H., Deng, T., et al.: Improving IIoT security: unveiling threats through advanced side-channel analysis. Comput. Secur. 148, 104135 (2025). https:\/\/doi.org\/10.1016\/j.cose.2024.104135","journal-title":"Comput. Secur."},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Hink, R.C.B., Beaver, J.M., Buckner, M.A., Morris, T., Adhikari, U., Pan, S.: Machine learning for power system disturbance and cyber-attack discrimination. In: 2014 7th International Symposium on Resilient Control Systems (ISRCS), pp. 19\u201321. IEEE (2014). https:\/\/doi.org\/10.1109\/ISRCS.2014.6900095","DOI":"10.1109\/ISRCS.2014.6900095"},{"key":"12_CR13","doi-asserted-by":"publisher","first-page":"13960","DOI":"10.1109\/ACCESS.2019.2894819","volume":"7","author":"E Hossain","year":"2019","unstructured":"Hossain, E., Khan, I., Un-Noor, F., Sikander, S.S., Sunny, M.S.H.: Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access 7, 13960\u201313988 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2894819","journal-title":"IEEE Access"},{"key":"12_CR14","doi-asserted-by":"publisher","first-page":"177447","DOI":"10.1109\/ACCESS.2020.3026923","volume":"8","author":"A Huseinovi\u0107","year":"2020","unstructured":"Huseinovi\u0107, A., Mrdovi\u0107, S., Bicakci, K., Uludag, S.: A survey of denial-of-service attacks and solutions in the smart grid. IEEE Access 8, 177447\u2013177470 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3026923","journal-title":"IEEE Access"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Judge, M.A., Khan, A., Manzoor, A., Khattak, H.A.: Overview of smart grid implementation: frameworks, impact, performance and challenges. J. Energy Storage 104056 (2022). https:\/\/doi.org\/10.1016\/j.est.2022.104056","DOI":"10.1016\/j.est.2022.104056"},{"issue":"13","key":"12_CR16","doi-asserted-by":"publisher","first-page":"11604","DOI":"10.1109\/JIOT.2021.3130156","volume":"9","author":"IA Khan","year":"2021","unstructured":"Khan, I.A., Moustafa, N., Pi, D., Sallam, K.M., Zomaya, A.Y., Li, B.: A new explainable deep learning framework for cyber threat discovery in industrial IoT networks. IEEE Internet Things J. 9(13), 11604\u201311613 (2021). https:\/\/doi.org\/10.1109\/JIOT.2021.3130156","journal-title":"IEEE Internet Things J."},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Khan, M.A., Saleh, A.M., Waseem, M., Sajjad, I.A.: Artificial intelligence enabled demand response: prospects and challenges in smart grid environment. IEEE Access 1477\u20131505 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3231444","DOI":"10.1109\/ACCESS.2022.3231444"},{"issue":"3","key":"12_CR18","doi-asserted-by":"publisher","first-page":"5224","DOI":"10.1109\/JIOT.2019.2899492","volume":"6","author":"F Li","year":"2019","unstructured":"Li, F., Shi, Y., Shinde, A., Ye, J., Song, W.: Enhanced cyber-physical security in internet of things through energy auditing. IEEE Internet Things J. 6(3), 5224\u20135231 (2019). https:\/\/doi.org\/10.1109\/JIOT.2019.2899492","journal-title":"IEEE Internet Things J."},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 22\u201329. IEEE (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"12_CR20","doi-asserted-by":"publisher","first-page":"44023","DOI":"10.1109\/ACCESS.2024.3370911","volume":"12","author":"SH Mohammed","year":"2024","unstructured":"Mohammed, S.H., Al-Jumaily, A., Singh, M.S.J., Jim\u00e9nez, V.P.G., Jaber, A.S., Hussein, Y.S.: A review on the evaluation of feature selection using machine learning for cyber-attack detection in smart grid. IEEE Access 12, 44023\u201344042 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3370911","journal-title":"IEEE Access"},{"issue":"3","key":"12_CR21","doi-asserted-by":"publisher","first-page":"2218","DOI":"10.1109\/TSG.2019.2949998","volume":"11","author":"AS Musleh","year":"2019","unstructured":"Musleh, A.S., Chen, G., Dong, Z.Y.: A survey on the detection algorithms for false data injection attacks in smart grids. IEEE Trans. Smart Grid 11(3), 2218\u20132234 (2019). https:\/\/doi.org\/10.1109\/TSG.2019.2949998","journal-title":"IEEE Trans. Smart Grid"},{"issue":"10","key":"12_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3565570","volume":"55","author":"MN Nafees","year":"2023","unstructured":"Nafees, M.N., Saxena, N., Cardenas, A., Grijalva, S., Burnap, P.: Smart grid cyber-physical situational awareness of complex operational technology attacks: a review. ACM Comput. Surv. 55(10), 1\u201336 (2023). https:\/\/doi.org\/10.1145\/3565570","journal-title":"ACM Comput. Surv."},{"key":"12_CR23","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1784","volume":"10","author":"SSA Naqvi","year":"2024","unstructured":"Naqvi, S.S.A., Li, Y., Uzair, M.: DDoS attack detection in smart grid network using reconstructive machine learning models. PeerJ Comput. Sci. 10, e1784 (2024). https:\/\/doi.org\/10.7717\/peerj-cs.1784","journal-title":"PeerJ Comput. Sci."},{"issue":"2","key":"12_CR24","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1109\/TSG.2021.3127518","volume":"13","author":"RR Nejad","year":"2021","unstructured":"Nejad, R.R., Sun, W.: Enhancing active distribution systems resilience by fully distributed self-healing strategy. IEEE Trans. Smart Grid 13(2), 1023\u20131034 (2021). https:\/\/doi.org\/10.1109\/TSG.2021.3127518","journal-title":"IEEE Trans. Smart Grid"},{"issue":"1","key":"12_CR25","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/s40565-016-0257-9","volume":"5","author":"L Ni","year":"2017","unstructured":"Ni, L., Wen, F., Liu, W., Meng, J., Lin, G., Dang, S.: Congestion management with demand response considering uncertainties of distributed generation outputs and market prices. J. Mod. Power Syst. Clean Energy 5(1), 66\u201378 (2017). https:\/\/doi.org\/10.1007\/s40565-016-0257-9","journal-title":"J. Mod. Power Syst. Clean Energy"},{"issue":"2","key":"12_CR26","doi-asserted-by":"publisher","first-page":"548","DOI":"10.3390\/smartcities4020029","volume":"4","author":"OA Omitaomu","year":"2021","unstructured":"Omitaomu, O.A., Niu, H.: Artificial intelligence techniques in smart grid: a survey. Smart Cities 4(2), 548\u2013568 (2021). https:\/\/doi.org\/10.3390\/smartcities4020029","journal-title":"Smart Cities"},{"key":"12_CR27","doi-asserted-by":"publisher","first-page":"46595","DOI":"10.1109\/ACCESS.2019.2909807","volume":"7","author":"PI Radoglou-Grammatikis","year":"2019","unstructured":"Radoglou-Grammatikis, P.I., Sarigiannidis, P.G.: Securing the smart grid: a comprehensive compilation of intrusion detection and prevention systems. IEEE Access 7, 46595\u201346620 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2909807","journal-title":"IEEE Access"},{"key":"12_CR28","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.aej.2024.10.009","volume":"110","author":"M Ragab","year":"2025","unstructured":"Ragab, M., Basheri, M., Albogami, N.N., et al.: Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT. Alex. Eng. J. 110, 438\u2013450 (2025). https:\/\/doi.org\/10.1016\/j.aej.2024.10.009","journal-title":"Alex. Eng. J."},{"issue":"6","key":"12_CR29","doi-asserted-by":"publisher","first-page":"4729","DOI":"10.1109\/TSG.2023.3253723","volume":"14","author":"S Rath","year":"2023","unstructured":"Rath, S., Nguyen, L.D., Sahoo, S., Popovski, P.: Self-healing secure blockchain framework in microgrids. IEEE Trans. Smart Grid 14(6), 4729\u20134740 (2023). https:\/\/doi.org\/10.1109\/TSG.2023.3253723","journal-title":"IEEE Trans. Smart Grid"},{"issue":"14s","key":"12_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3592797","volume":"55","author":"HT Reda","year":"2023","unstructured":"Reda, H.T., Anwar, A., Mahmood, A.N., Tari, Z.: A taxonomy of cyber defence strategies against false data attacks in smart grids. ACM Comput. Surv. 55(14s), 1\u201337 (2023). https:\/\/doi.org\/10.1145\/3592797","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"12_CR31","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1109\/JSYST.2023.3248320","volume":"17","author":"F Sangoleye","year":"2023","unstructured":"Sangoleye, F., Jao, J., Faris, K., Tsiropoulou, E.E., Papavassiliou, S.: Reinforcement learning-based demand response management in smart grid systems with prosumers. IEEE Syst. J. 17(2), 1797\u20131807 (2023). https:\/\/doi.org\/10.1109\/JSYST.2023.3248320","journal-title":"IEEE Syst. J."},{"key":"12_CR32","doi-asserted-by":"publisher","unstructured":"Shtayat, M.M., Hasan, M.K., Sulaiman, R., et\u00a0al.: An explainable ensemble deep learning approach for intrusion detection in industrial internet of things. IEEE Access 115047\u2013115061 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3323573","DOI":"10.1109\/ACCESS.2023.3323573"},{"issue":"3","key":"12_CR33","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.1109\/TNSE.2021.3099371","volume":"8","author":"D Upadhyay","year":"2021","unstructured":"Upadhyay, D., Manero, J., Zaman, M., Sampalli, S.: Intrusion detection in SCADA based power grids: recursive feature elimination model with majority vote ensemble algorithm. IEEE Trans. Netw. Sci. Eng. 8(3), 2559\u20132574 (2021). https:\/\/doi.org\/10.1109\/TNSE.2021.3099371","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"12_CR34","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/OJIA.2024.3365576","volume":"5","author":"M Usama","year":"2024","unstructured":"Usama, M., Aman, M.N.: Command injection attacks in smart grids: a survey. IEEE Open J. Ind. Appl. 5, 75\u201385 (2024). https:\/\/doi.org\/10.1109\/OJIA.2024.3365576","journal-title":"IEEE Open J. Ind. Appl."},{"issue":"10","key":"12_CR35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0308639","volume":"19","author":"J Wang","year":"2024","unstructured":"Wang, J., Yang, K., Li, M.: NIDS-FGPA: a federated learning network intrusion detection algorithm based on secure aggregation of gradient similarity models. PLoS ONE 19(10), e0308639 (2024). https:\/\/doi.org\/10.1371\/journal.pone.0308639","journal-title":"PLoS ONE"},{"key":"12_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109572","volume":"119","author":"J Wang","year":"2024","unstructured":"Wang, J., et al.: FDIA localization and classification detection in smart grids using multi-modal data and deep learning technique. Comput. Electr. Eng. 119, 109572 (2024). https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109572","journal-title":"Comput. Electr. Eng."},{"issue":"5","key":"12_CR37","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TSMC.2022.3218039","volume":"53","author":"M Yu","year":"2022","unstructured":"Yu, M., Jiang, J., Ye, X., Zhang, X., Lee, C., Hong, S.H.: Demand response flexibility potential trading in smart grids: a multileader multifollower stackelberg game approach. IEEE Trans. Syst. Man Cybern. Syst. 53(5), 2664\u20132675 (2022). https:\/\/doi.org\/10.1109\/TSMC.2022.3218039","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"12_CR38","doi-asserted-by":"publisher","unstructured":"Zhang, L., Gao, Y., Zhu, H., Tao, L.: A distributed real-time pricing strategy based on reinforcement learning approach for smart grid. Expert Syst. Appl. 116285 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2021.116285","DOI":"10.1016\/j.eswa.2021.116285"}],"container-title":["Lecture Notes in Computer Science","Foundations and Practice of Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-20026-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T23:43:48Z","timestamp":1778456628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-20026-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032200259","9783032200266"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-20026-6_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"FPS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Foundations and Practice of Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"25 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fps2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hub.imt-atlantique.fr\/fps2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}