{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:02:01Z","timestamp":1774368121515,"version":"3.50.1"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031530241","type":"print"},{"value":"9783031530258","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53025-8_19","type":"book-chapter","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T20:02:12Z","timestamp":1706731332000},"page":"269-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["On the\u00a0Use of\u00a0VGs for\u00a0Feature Selection in\u00a0Supervised Machine Learning - A Use Case to\u00a0Detect Distributed DoS Attacks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1153-5145","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Lopes","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5586-0321","authenticated-orcid":false,"given":"Alberto","family":"Partida","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1856-6101","authenticated-orcid":false,"given":"Pedro","family":"Pinto","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5583-5772","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Pinto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"key":"19_CR1","first-page":"799","volume":"111","author":"LB Abbas","year":"2020","unstructured":"Abbas, L.B., Sadiq, M.A., Ahmad, M.O.: Machine learning-based detection of DDoS attacks: a review. Futur. Gener. Comput. Syst. 111, 799\u2013811 (2020)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"19_CR2","volume-title":"Introduction to Machine Learning","author":"E Alpaydin","year":"2010","unstructured":"Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2010)"},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Asonye, EA., Anwuna, I., Musa, S.M.: Securing Zig-Bee IoT network against HULK distributed denial of service attack. In: 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), pp. 156\u2013162 (2020). https:\/\/doi.org\/10.1109\/HONET50430.2020.9322808","DOI":"10.1109\/HONET50430.2020.9322808"},{"key":"19_CR4","unstructured":"Bagheri, R.: Introduction to SHAP Values and their Application in Machine Learning. Towards Data Science (2022). https:\/\/towardsdatascience.com\/introduction-to-shap-values-and-their-application-in-machine-learning-8003718e6827"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Barrera-Animas, A.Y., et al.: Rainfall prediction: a comparative analysis of modern machine learning algorithms for time-series forecasting. Mach. Learn. Appl. 7, 100204 (2022). ISSN 2666-8270. https:\/\/doi.org\/10.1016\/j.mlwa.2021.100204","DOI":"10.1016\/j.mlwa.2021.100204"},{"key":"19_CR6","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. In: International Conference on Learning Representations (2012)"},{"key":"19_CR7","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Boccaletti, S., et al.: Complex networks: structure and dynamics. Phys. Rep. 424(4), 175\u2013308 (2006). ISSN 0370-1573. https:\/\/doi.org\/10.1016\/j.physrep.2005.10.009","DOI":"10.1016\/j.physrep.2005.10.009"},{"issue":"4\u20135","key":"19_CR9","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.physrep.2005.10.009","volume":"424","author":"S Boccaletti","year":"2006","unstructured":"Boccaletti, S., et al.: Complex networks: structure and dynamics. Phys. Rep. 424(4\u20135), 175\u2013308 (2006)","journal-title":"Phys. Rep."},{"key":"19_CR10","unstructured":"Brown, C.: Data division strategies in machine learning. In: Proceedings of the International Conference on Machine Learning, pp. 234\u2013245 (2017)"},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"Chippalakatti, S., Renumadhavi, C.H., Pallavi, A.: Comparison of unsupervised machine learning Algorithm F or dimensionality reduction. In: 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), pp. 1\u20137 (2022). https:\/\/doi.org\/10.1109\/ICKECS56523.2022.10060625.","DOI":"10.1109\/ICKECS56523.2022.10060625."},{"issue":"3","key":"19_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/bf00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995). https:\/\/doi.org\/10.1007\/bf00994018","journal-title":"Mach. Learn."},{"issue":"1","key":"19_CR13","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21\u201327 (1967). https:\/\/doi.org\/10.1109\/TIT.1967.1053964","journal-title":"IEEE Trans. Inf. Theor."},{"issue":"10","key":"19_CR14","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78\u201387 (2012)","journal-title":"Commun. ACM"},{"key":"19_CR15","unstructured":"Falkner, S., Klein, A., Hutter, F.: BOHB: robust and efficient hyperparameter optimization at scale. In: Proceedings of the 35th International Conference on Machine Learning, pp. 1436\u20131445 (2018)"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(2), 179\u2013188. 1469\u20131809 (1936). https:\/\/doi.org\/10.1111\/j.1469-1809.1936.tb02137.x.","DOI":"10.1111\/j.1469-1809.1936.tb02137.x."},{"key":"19_CR17","unstructured":"Gani, A., Ullah, S., Khan, K.: Detection of Denial of Service (DoS) attacks using machine learning techniques. In: 2019 International Conference on Computer and Information Sciences (ICCIS), pp. 1\u20136. IEEE (2019)"},{"key":"19_CR18","unstructured":"G\u00e9ron, A.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O\u2019Reilly Media (2019)"},{"key":"19_CR19","unstructured":"Gonzalez, M.: Algorithm Applications in Machine Learning. Springer, Heidelberg (2019)"},{"key":"19_CR20","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)"},{"key":"19_CR21","doi-asserted-by":"publisher","unstructured":"Gupta, B.B., Badve, O.P.: Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment. Neural Comput. Appl. 28(12 ), 3655\u20133682 (2017). ISSN 1433\u20133058. https:\/\/doi.org\/10.1007\/s00521-016-2317-5","DOI":"10.1007\/s00521-016-2317-5"},{"key":"19_CR22","doi-asserted-by":"publisher","unstructured":"Gupta, B., Gupta, R., Tyagi, S.K.: Taxonomy of DDoS attacks and their prevention techniques: a review. J. Netw. Comput. Appl. 126, 48\u201373 (2019). ISSN 1084-8045. https:\/\/doi.org\/10.1016\/j.jnca.2018.10.009","DOI":"10.1016\/j.jnca.2018.10.009"},{"key":"19_CR23","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS, Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7"},{"key":"19_CR24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2013.08.034","volume":"254","author":"SMR Islam","year":"2014","unstructured":"Islam, S.M.R., et al.: Detecting DDoS attacks with machine learning techniques. Inf. Sci. 254, 1\u201314 (2014)","journal-title":"Inf. Sci."},{"issue":"8","key":"19_CR25","first-page":"933","volume":"21","author":"M Johnson","year":"2015","unstructured":"Johnson, M., Smith, L.: Visibility graphs: a survey. IEEE Trans. Vis. Comput. Graph. 21(8), 933\u2013952 (2015)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"issue":"2","key":"19_CR26","first-page":"789","volume":"8","author":"M Jones","year":"2016","unstructured":"Jones, M., Brown, E.: Data pre-processing techniques in machine learning. Int. J. Data Sci. 8(2), 789\u2013804 (2016)","journal-title":"Int. J. Data Sci."},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Kelleher, J.D., Tierney, B., Tierney, B.: Data Science: An Introduction, 2nd edn. CRC Press (2018). Chap. 5","DOI":"10.7551\/mitpress\/11140.001.0001"},{"key":"19_CR28","doi-asserted-by":"publisher","unstructured":"Khosravi, A., Machado, L., Nunes, R.O.: Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil. Appl. Energy 224, 550\u2013566 (2018). ISSN 0306-2619. https:\/\/doi.org\/10.1016\/j.apenergy.2018.05.043","DOI":"10.1016\/j.apenergy.2018.05.043"},{"issue":"13","key":"19_CR29","doi-asserted-by":"publisher","first-page":"4972","DOI":"10.1073\/pnas.0709247105","volume":"105","author":"L Lacasa","year":"2008","unstructured":"Lacasa, L., et al.: From time series to complex networks: the visibility graph. Proc. Natl. Acad. Sci. 105(13), 4972\u20134975 (2008)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"4","key":"19_CR30","doi-asserted-by":"crossref","first-page":"041101","DOI":"10.1063\/1.5028373","volume":"28","author":"J Liu","year":"2018","unstructured":"Liu, J., Chen, J.: Visibility graphs for analyzing complex systems: a review. Chaos Interdisc. J. Nonlinear Sci. 28(4), 041101 (2018)","journal-title":"Chaos Interdisc. J. Nonlinear Sci."},{"key":"19_CR31","unstructured":"Lucas, T., da Fontoura Costa, L., da Rocha, L.E.C.: Visibility graph analysis: a review. J. Stat. Mech. Theor. Exp. 2014(8), 08001 (2014)"},{"key":"19_CR32","doi-asserted-by":"publisher","unstructured":"Mangalathu, S., Hwang, S.-H., Jeon, J.-S.: Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng. Struct. 219, 110927 (2020). ISSN 0141-0296. https:\/\/doi.org\/10.1016\/j.engstruct.2020.110927","DOI":"10.1016\/j.engstruct.2020.110927"},{"key":"19_CR33","unstructured":"McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, pp. 41\u201348 (1998)"},{"key":"19_CR34","doi-asserted-by":"publisher","unstructured":"Mishra, D.K., Singh, V.P., Tripathi, R.: Network security situation awareness using visibility graph. J. Netw. Comput. Appl. 58, 49\u201362 (2015). ISSN 1084-8045. https:\/\/doi.org\/10.1016\/j.jnca.2015.09.007","DOI":"10.1016\/j.jnca.2015.09.007"},{"key":"19_CR35","unstructured":"M\u00fcller, A.C., Guido, S.: Introduction to Machine Learning with Python: A Guide for Data Scientists. O\u2019Reilly Media (2016)"},{"key":"19_CR36","series-title":"Springer Briefs in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41063-0","volume-title":"Support Vector Machines and Perceptrons. Learning, Optimization, Classification, and Application to Social Networks","author":"MN Murty","year":"2016","unstructured":"Murty, M.N., Raghava, R.: Support Vector Machines and Perceptrons. Learning, Optimization, Classification, and Application to Social Networks. SCS, Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41063-0"},{"issue":"6","key":"19_CR37","first-page":"275","volume":"18","author":"AJ Myles","year":"2004","unstructured":"Myles, A.J., et al.: An introduction to decision tree modeling. J. Chemom. J. Chemometr. Soc. 18(6), 275\u2013285 (2004)","journal-title":"J. Chemom. J. Chemometr. Soc."},{"key":"19_CR38","doi-asserted-by":"publisher","unstructured":"Nasteski, V.: An overview of the supervised machine learning methods. In: HORIZONS.B 4, pp. 51\u201362, December 2017. https:\/\/doi.org\/10.20544\/HORIZONS.B.04.1.17.P05","DOI":"10.20544\/HORIZONS.B.04.1.17.P05"},{"issue":"2","key":"19_CR39","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1137\/S003614450342480","volume":"45","author":"MEJ Newman","year":"2003","unstructured":"Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167\u2013257 (2003)","journal-title":"SIAM Rev."},{"key":"19_CR40","unstructured":"Ng, A.: Machine learning yearning. Draft (2018). https:\/\/www.mlyearning.org\/"},{"key":"19_CR41","doi-asserted-by":"publisher","unstructured":"Partida, A., Criado, R., Romance, M.: Visibility graph analysis of IOTA and IoTeX price series: an intentional risk-based strategy to use 5G for IoT. Electronics 10(18) (2021). ISSN 2079-9292. https:\/\/doi.org\/10.3390\/electronics10182282","DOI":"10.3390\/electronics10182282"},{"key":"19_CR42","doi-asserted-by":"publisher","unstructured":"Partida, A., et al.: The chaotic, self-similar and hierarchical patterns in Bitcoin and Ethereum price series. Chaos Solitons Fractals 165, 112806 (2022). ISSN 0960-0779. https:\/\/doi.org\/10.1016\/j.chaos.2022.112806","DOI":"10.1016\/j.chaos.2022.112806"},{"issue":"6088","key":"19_CR43","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986). https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"key":"19_CR44","doi-asserted-by":"publisher","unstructured":"\u0161ar\u010devi\u0107, A., et al.: Cybersecurity knowledge extraction using XAI. Appl. Sci. 12(17) (2022). ISSN 2076-3417. https:\/\/doi.org\/10.3390\/app12178669","DOI":"10.3390\/app12178669"},{"key":"19_CR45","doi-asserted-by":"publisher","unstructured":"Shorey, T., et al.: Performance comparison and analysis of Slowloris, GoldenEye and Xerxes DDoS attack tools. In: 2018 International Conference on Advances in Computing, Communications and Informatics, ICA CCI 2018, pp. 318\u2013322 (2018). https:\/\/doi.org\/10.1109\/ICACCI.2018.8554590","DOI":"10.1109\/ICACCI.2018.8554590"},{"issue":"4","key":"19_CR46","first-page":"1234","volume":"12","author":"J Smith","year":"2018","unstructured":"Smith, J., Johnson, S.: Data collection for machine learning. J. Mach. Learn. Res. 12(4), 1234\u20131256 (2018)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR47","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: International Conference on Neural Information Processing Systems (2012)"},{"issue":"4","key":"19_CR48","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1063\/1.2801687","volume":"17","author":"B Stefano","year":"2007","unstructured":"Stefano, B.: Multiscale vulnerability of complex networks. Chaos 17(4), 175\u2013308 (2007). https:\/\/doi.org\/10.1063\/1.2801687","journal-title":"Chaos"},{"key":"19_CR49","unstructured":"Wang, X., Zhang, W.: Visibility graph analysis: a novel approach for network traffic modeling. In: Proceedings of the International Conference on Communications, pp. 123\u2013130 (2017)"},{"key":"19_CR50","unstructured":"Warda: Application-Layer DDoS Dataset (2020). https:\/\/www.kaggle.com\/datasets\/wardac\/applicationlayer-ddos-dataset?select=test_mosaic.csv"},{"issue":"6","key":"19_CR51","doi-asserted-by":"crossref","first-page":"062817","DOI":"10.1103\/PhysRevE.92.062817","volume":"92","author":"J Xiang","year":"2015","unstructured":"Xiang, J., Small, M.: Visibility graphlet approach to chaotic time series. Phys. Rev. E 92(6), 062817 (2015)","journal-title":"Phys. Rev. E"},{"key":"19_CR52","doi-asserted-by":"publisher","first-page":"238701","DOI":"10.1103\/PhysRevLett.96.238701","volume":"96","author":"J Zhang","year":"2006","unstructured":"Zhang, J., Small, M.: Complex network from pseudoperiodic time series: topology versus dynamics. Phys. Rev. Lett. 96, 238701 (2006). https:\/\/doi.org\/10.1103\/PhysRevLett.96.238701","journal-title":"Phys. Rev. Lett."}],"container-title":["Communications in Computer and Information Science","Optimization, Learning Algorithms and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53025-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T20:15:34Z","timestamp":1706732134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53025-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031530241","9783031530258"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53025-8_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OL2A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization, Learning Algorithms and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ponta Delgada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ol2a2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ol2a.ipb.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"162","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":"66","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":"41% - 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":"3","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","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)"}}]}}