{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T11:36:32Z","timestamp":1787657792160,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030948993","type":"print"},{"value":"9783030949006","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-94900-6_11","type":"book-chapter","created":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T08:03:48Z","timestamp":1642579428000},"page":"222-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Using MedBIoT Dataset to\u00a0Build Effective Machine Learning-Based IoT Botnet Detection Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3655-5804","authenticated-orcid":false,"given":"Alejandro","family":"Guerra-Manzanares","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Medina-Galindo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8882-4095","authenticated-orcid":false,"given":"Hayretdin","family":"Bahsi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5571-1692","authenticated-orcid":false,"given":"Sven","family":"N\u00f5mm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,19]]},"reference":[{"key":"11_CR1","unstructured":"Antonakakis, M., et al.: Understanding the mirai botnet. In: 26th $$USENIX$$ Security Symposium ($$\\{USENIX\\}$$ Security 17). pp. 1093\u20131110 (2017)"},{"key":"11_CR2","unstructured":"Asokan, A.: Massive botnet attack used more than 400,000 IoT devices (2019). https:\/\/www.bankinfosecurity.com\/massive-botnet-attack-used-more-than-400000-iot-devices-a-12841"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Bah\u015fi, H., N\u00f5mm, S., La Torre, F.B.: Dimensionality reduction for machine learning based IoT botnet detection. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1857\u20131862 (2018)","DOI":"10.1109\/ICARCV.2018.8581205"},{"issue":"4","key":"11_CR4","doi-asserted-by":"publisher","first-page":"3496","DOI":"10.1109\/COMST.2018.2844742","volume":"20","author":"E Benkhelifa","year":"2018","unstructured":"Benkhelifa, E., Welsh, T., Hamouda, W.: A critical review of practices and challenges in intrusion detection systems for IoT: toward universal and resilient systems. IEEE Commun. Surv. Tutor. 20(4), 3496\u20133509 (2018)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MC.2017.62","volume":"2","author":"E Bertino","year":"2017","unstructured":"Bertino, E., Islam, N.: Botnets and internet of things security. Computer 2, 76\u201379 (2017)","journal-title":"Computer"},{"key":"11_CR6","unstructured":"Bezerra, V.H., da Costa, V.G.T., Martins, R.A., Junior, S.B., Miani, R.S., Zarpelao, B.B.: Data set (2018). http:\/\/www.uel.br\/grupo-pesquisa\/secmq\/dataset-iot-security.html"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Bezerra, V.H., da Costa, V.G.T., Martins, R.A., Junior, S.B., Miani, R.S., Zarpelao, B.B.: Providing IoT host-based datasets for intrusion detection research. In: Anais do XVIII Simp\u00f3sio Brasileiro em Seguran\u00e7a da Informa\u00e7\u00e3o e de Sistemas Computacionais, pp. 15\u201328. SBC (2018)","DOI":"10.5753\/sbseg.2018.4240"},{"key":"11_CR8","doi-asserted-by":"publisher","DOI":"10.3990\/1.9789036528535","volume-title":"Revisiting Anomaly-based Network Intrusion Detection Systems","author":"D Bolzoni","year":"2009","unstructured":"Bolzoni, D.: Revisiting Anomaly-based Network Intrusion Detection Systems. University of Twente, Enschede (2009)"},{"key":"11_CR9","unstructured":"Bonderud, D.: Leaked mirai malware boosts IoT insecurity threat level (2016). https:\/\/securityintelligence.com\/news\/leaked-mirai-mal ware-boosts-iot-insecurity-threat-level\/"},{"key":"11_CR10","unstructured":"Bosche, A., Crawford, D., Jackson, D., Schallehn, M., Schorling, C.: Unlocking opportunities in the internet of things (2018). https:\/\/www.bain.com\/contentassets\/5aa3a678438846 289af59f62e62a3456\/bain_brief_unlocking_opportunit ies_in_the_internet_of_things.pdf"},{"issue":"1","key":"11_CR11","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/SURV.2013.050113.00191","volume":"16","author":"I Butun","year":"2013","unstructured":"Butun, I., Morgera, S.D., Sankar, R.: A survey of intrusion detection systems in wireless sensor networks. IEEE Commun. Surv. Tutor. 16(1), 266\u2013282 (2013)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"11_CR12","unstructured":"Crowdstrike: Hybrid analysis (2019). https:\/\/www.hybrid-analysis.com\/"},{"key":"11_CR13","unstructured":"DeBeck, C., Chung, J., McMillen, D.: I can\u2019t believe mirais: tracking the infamous IoT malware (2019). https:\/\/securityintelligence.com\/posts\/i-cant-believe-mirais-tracking-the-infamous-iot-malware-2\/"},{"key":"11_CR14","unstructured":"Doffman, Z.: Cyberattacks on IoT devices surge 300% in 2019, \u2018measured in billions\u2019, report claims (2019). https:\/\/www.forbes.com\/sites\/zakdoffman\/2019\/09\/14\/ dangerous-cyberattacks-on-iot-devices-up-300-in-2019-now-rampant-report-claims\/#574229995892"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Doshi, R., Apthorpe, N., Feamster, N.: Machine learning DDoS detection for consumer internet of things devices. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 29\u201335. IEEE (2018)","DOI":"10.1109\/SPW.2018.00013"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Feily, M., Shahrestani, A., Ramadass, S.: A survey of botnet and botnet detection. In: 2009 Third International Conference on Emerging Security Information, Systems and Technologies, pp. 268\u2013273. IEEE (2009)","DOI":"10.1109\/SECURWARE.2009.48"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Garcia, S., Grill, M., Stiborek, J., Zunino, A.: An empirical comparison of botnet detection methods. Compu. Secur. 45, 100\u2013123 (2014)","DOI":"10.1016\/j.cose.2014.05.011"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Gifford, W.R., Goldberg, M.L., Tanimoto, P.M., Celnicker, D.R., Poplawski, M.E.: Residential lighting end-use consumption study: estimation framework and initial estimates (2012). https:\/\/www1.eere.energy.gov\/buildings\/publications\/pdfs\/ssl\/2012_residential-lighting-study.pdf","DOI":"10.2172\/1162372"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Guerra-Manzanares, A., Bahsi, H., N\u00f5mm, S.: Hybrid feature selection models for machine learning based botnet detection in IoT networks. In: 2019 International Conference on Cyberworlds (CW), pp. 324\u2013327 (2019)","DOI":"10.1109\/CW.2019.00059"},{"key":"11_CR20","unstructured":"Guerra-Manzanares, A., Medina-Galindo, J., Bahsi, H., N\u00f5mm, S.: Medbiot data set archive (2020). https:\/\/cs.taltech.ee\/research\/data\/medbiot\/"},{"key":"11_CR21","doi-asserted-by":"publisher","unstructured":"Guerra-Manzanares, A., Medina-Galindo, J., Bahsi, H., N\u00f5mm, S.: Medbiot: generation of an IoT botnet dataset in a medium-sized IoT network. In: Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, pp. 207\u2013218. INSTICC, SciTePress (2020). https:\/\/doi.org\/10.5220\/0009187802070218","DOI":"10.5220\/0009187802070218"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Hachem, N., Mustapha, Y.B., Granadillo, G.G., Debar, H.: Botnets: lifecycle and taxonomy. In: 2011 Conference on Network and Information Systems Security, pp. 1\u20138. IEEE (2011)","DOI":"10.1109\/SAR-SSI.2011.5931395"},{"key":"11_CR23","unstructured":"Hilton, S.: DYN analysis summary of Friday October 21 attack (2016). https:\/\/dyn.com\/blog\/dyn-analysis-summary-of-friday-october-21-attack\/"},{"key":"11_CR24","unstructured":"Kang, H., Ahn, D.H., Lee, G.M., Yoo, J.D., Park, K.H., Kim, H.K.: IoT network intrusion dataset(2019). http:\/\/dx.doi.org\/10.21227\/q70p-q449"},{"issue":"7","key":"11_CR25","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MC.2017.201","volume":"50","author":"C Kolias","year":"2017","unstructured":"Kolias, C., Kambourakis, G., Stavrou, A., Voas, J.: Ddos in the IoT: Mirai and other botnets. Computer 50(7), 80\u201384 (2017)","journal-title":"Computer"},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset. Fut. Gene. Comput. Syst. 100, 779\u2013796 (2019)","journal-title":"Fut. Gene. Comput. Syst."},{"key":"11_CR27","unstructured":"Krebs, B.: Krebsonsecurity hit with record Ddos (2016). https:\/\/krebsonsecurity.com\/2016\/09\/krebsonsecurity-hit-with-record-ddos\/"},{"key":"11_CR28","unstructured":"Kroustek, J., Iliushin, V., Shirokova, A., Neduchal, J., Hron, M.: Torii botnet - not another mirai variant (2018). https:\/\/blog.avast.com\/new-torii-botnet-threat-research"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Leonard, J., Xu, S., Sandhu, R.: A framework for understanding botnets. In: 2009 International Conference on Availability, Reliability and Security, pp. 917\u2013922. IEEE (2009)","DOI":"10.1109\/ARES.2009.65"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Lin, K.C., Chen, S.Y., Hung, J.C.: Botnet detection using support vector machines with artificial fish swarm algorithm. J. Appl. Math. 2014 (2014)","DOI":"10.1155\/2014\/986428"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Livadas, C., Walsh, R., Lapsley, D.E., Strayer, W.T.: Using machine learning techniques to identify botnet traffic. In: LCN, pp. 967\u2013974. Citeseer (2006)","DOI":"10.1109\/LCN.2006.322210"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Marzano, A., et al.: The evolution of bashlite and mirai IoT botnets. In: 2018 IEEE Symposium on Computers and Communications (ISCC), pp. 00813\u201300818. IEEE (2018)","DOI":"10.1109\/ISCC.2018.8538636"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"McDermott, C.D., Majdani, F., Petrovski, A.V.: Botnet detection in the internet of things using deep learning approaches. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489489"},{"key":"11_CR34","unstructured":"McKinsey: What\u2019s new with the internet of things? (2017). https:\/\/www.mckinsey.com\/industries\/semiconductors\/our-insights\/whats-new-with-the-internet-of-things"},{"key":"11_CR35","unstructured":"Meidan, Y., et al.: detection_of_iot_botnet_attacks_n_baiot data set (2018). http:\/\/archive.ics.uci.edu\/ml\/datasets\/detection_of_IoT_botnet_attacks_N_BaIoT"},{"issue":"3","key":"11_CR36","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","volume":"17","author":"Y Meidan","year":"2018","unstructured":"Meidan, Y., et al.: N-baiot-network-based detection of IoT botnet attacks using deep autoencoders. IEEE Perva. Comput. 17(3), 12\u201322 (2018)","journal-title":"IEEE Perva. Comput."},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089 (2018)","DOI":"10.14722\/ndss.2018.23204"},{"key":"11_CR38","unstructured":"Moustafa, N.: The bot-IoT dataset. http:\/\/dx.doi.org\/10.21227\/r7v2-x988 (2019). 10.21227\/r7v2-x988"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"N\u00f5mm, S., Bah\u015fi, H.: Unsupervised anomaly based botnet detection in IoT networks. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1048\u20131053 (2018)","DOI":"10.1109\/ICMLA.2018.00171"},{"key":"11_CR40","unstructured":"O\u2019Donnell, L.: More than half of IoT devices vulnerable to severe attacks (2020). https:\/\/threatpost.com\/half-iot-devices-vulnerable-severe-attacks\/153609\/"},{"key":"11_CR41","unstructured":"Parmisano, A., Garcia, S., Erquiaga, M.J.: Stratosphere laboratory. a labeled dataset with malicious and benign IoT network traffic (2020). https:\/\/www.stratosphereips.org\/datasets-iot23"},{"key":"11_CR42","unstructured":"Pratt, M.K.: Top challenges of IoT adoption in the enterprise (2019). https:\/\/internetofthingsagenda.techtarget.com\/feature\/Top-challenges-of-IoT-adoption-in-the-enterprise"},{"key":"11_CR43","unstructured":"Pritchard, M.: Ddos attack timeline: time to take Ddos seriously (2018). https:\/\/activereach.net\/newsroom\/blog\/time-to-take-ddos-seriously-a-recent-timeline-of-events\/"},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"Prokofiev, A.O., Smirnova, Y.S., Surov, V.A.: A method to detect internet of things botnets. In: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 105\u2013108. IEEE (2018)","DOI":"10.1109\/EIConRus.2018.8317041"},{"key":"11_CR45","unstructured":"Radware: A quick history of IoT botnets (2018). https:\/\/blog.radware.com\/uncategorized\/2018\/03\/history-of-iot-botnets\/"},{"key":"11_CR46","unstructured":"Scikit-Learn: novelty and outlier detection (2020). https:\/\/scikit-learn.org\/stable\/modules\/outlier_detection.html"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Shiravi, A., Shiravi, H., Tavallaee, M., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Secur. 31(3), 357\u2013374 (2012)","DOI":"10.1016\/j.cose.2011.12.012"},{"key":"11_CR48","doi-asserted-by":"publisher","unstructured":"Shire, R., Shiaeles, S., Bendiab, K., Ghita, B., Kolokotronis, N.: Malware squid: a novel iot malware traffic analysis framework using convolutional neural network and binary visualisation. In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems, pp. 65\u201376. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-01168-0","DOI":"10.1007\/978-3-030-01168-0"},{"issue":"2","key":"11_CR49","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.comnet.2012.07.021","volume":"57","author":"SS Silva","year":"2013","unstructured":"Silva, S.S., Silva, R.M., Pinto, R.C., Salles, R.M.: Botnets: a survey. Comput. Netw. 57(2), 378\u2013403 (2013)","journal-title":"Comput. Netw."},{"key":"11_CR50","doi-asserted-by":"crossref","unstructured":"Sklavos, N., Zaharakis, I.D., Kameas, A., Kalapodi, A.: Security & trusted devices in the context of internet of things (IoT). In: 2017 Euromicro Conference on Digital System Design (DSD), pp. 502\u2013509. IEEE (2017)","DOI":"10.1109\/DSD.2017.81"},{"key":"11_CR51","unstructured":"Statista: Forecast end-user spending on iot solutions worldwide from 2017 to 2025 (2019). https:\/\/www.statista.com\/statistics\/976313\/global-iot-market-size\/"},{"key":"11_CR52","unstructured":"Statista: Number of internet of things (IoT) connected devices worldwide in 2018, 2025 and 2030 (2019). https:\/\/www.statista.com\/statistics\/802690\/worldwide-connected-devices-by-access-technology\/"},{"issue":"5","key":"11_CR53","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/MWC.2007.4396943","volume":"14","author":"B Sun","year":"2007","unstructured":"Sun, B., Osborne, L., Xiao, Y., Guizani, S.: Intrusion detection techniques in mobile ad hoc and wireless sensor networks. IEEE Wirel. Commun. 14(5), 56\u201363 (2007)","journal-title":"IEEE Wirel. Commun."},{"key":"11_CR54","unstructured":"TrendMicro: Bashlite IoT malware updated with mining and backdoor commands, targets WeMo devices (2019)"},{"key":"11_CR55","unstructured":"Weagle, S.: Financial impact of mirai Ddos attack on DYN revealed in new data (2017). https:\/\/www.corero.com\/blog\/797-financial-impact-of-mirai-ddos-attack-on-dyn-revealed-in-new-data.html"},{"key":"11_CR56","unstructured":"Weisman, S.: Emerging threats - what is a distributed denial of service attack (Ddos) and what can you do about them? (2019). https:\/\/us.norton.com\/internetsecurity-emerging-threats-what-is-a-ddos-attack-30sectech-by-norton.html"},{"key":"11_CR57","unstructured":"Winward, R.: IoT attack handbook: A field guide to understanding IoT attacks from the mirai botnet to its modern variants (2018). https:\/\/www.datacom.cz\/userfiles\/miraihandbook ebook_final.pdf"},{"key":"11_CR58","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jnca.2017.02.009","volume":"84","author":"BB Zarpel\u00e3o","year":"2017","unstructured":"Zarpel\u00e3o, B.B., Miani, R.S., Kawakani, C.T., de Alvarenga, S.C.: A survey of intrusion detection in internet of things. J. Netw. Comput. Appl. 84, 25\u201337 (2017)","journal-title":"J. Netw. Comput. Appl."}],"container-title":["Communications in Computer and Information Science","Information Systems Security and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-94900-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T15:37:22Z","timestamp":1726501042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-94900-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030948993","9783030949006"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-94900-6_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICISSP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Systems Security and Privacy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valletta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","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":"25 February 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 February 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icissp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icissp.org\/?y=2020","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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","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":"11","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":"9% - 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":"3","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)"}}]}}