{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:42:03Z","timestamp":1742913723167,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031196461"},{"type":"electronic","value":"9783031196478"}],"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\/978-3-031-19647-8_13","type":"book-chapter","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T15:02:57Z","timestamp":1665759777000},"page":"173-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Systematic Review on\u00a0Phishing Detection: A Perspective Beyond a\u00a0High Accuracy in\u00a0Phishing Detection"],"prefix":"10.1007","author":[{"given":"Daniel Alejandro","family":"Barreiro Herrera","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge Eliecer","family":"Camargo Mendoza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"13_CR1","unstructured":"apwg: Phishing activity trends report Q4 2021 (2022). http:\/\/www.apwg.org"},{"key":"13_CR2","unstructured":"Athulya, A.A.: Towards the detection of phishing attacks Praveen K TIFAC-CORE in cyber security Amrita Vishwa Vidyapeetham (2020). ISBN 9781728155180"},{"key":"13_CR3","doi-asserted-by":"publisher","unstructured":"Patil, V., Thakkar, P., Shah, C., Bhat, T., Godse, S.P.: Detection and prevention of phishing websites using machine learning approach. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1\u20135 (2018). https:\/\/doi.org\/10.1109\/ICCUBEA.2018.8697412","DOI":"10.1109\/ICCUBEA.2018.8697412"},{"issue":"1","key":"13_CR4","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1109\/COMST.2019.2957750","volume":"22","author":"A Das","year":"2020","unstructured":"Das, A., Baki, S., Aassal, A.E., Verma, R., Dunbar, A.: SoK: a comprehensive reexamination of phishing research from the security perspective. IEEE Commun. Surv. Tutor. 22(1), 671\u2013708 (2020). https:\/\/doi.org\/10.1109\/COMST.2019.2957750. ISSN 1553-877X VO - 22","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Ya, J., Liu, T., Zhang, P., Shi, J., Guo, L., Gu, Z.: NeuralAS: DeepWord-based spoofed URLs detection against strong similar samples. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137 (2019). ISBN 2161-4407 VO. https:\/\/doi.org\/10.1109\/IJCNN.2019.8852416","DOI":"10.1109\/IJCNN.2019.8852416"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Nakamura, A., Dobashi, F.: Proactive phishing sites detection. In: IEEE\/WIC\/ACM International Conference on Web Intelligence, Series WI 2019, pp. 443\u2013448. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3350546.3352565. ISBN 9781450369343","DOI":"10.1145\/3350546.3352565"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Buber, E., Demir, \u00d6., Sahingoz, O.K.: Feature selections for the machine learning based detection of phishing websites. In: 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1\u20135 (2017). https:\/\/doi.org\/10.1109\/IDAP.2017.8090317. ISBN: VO","DOI":"10.1109\/IDAP.2017.8090317"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Adil, M., Khan, R., Ghani, M.A.N.U.: Preventive techniques of phishing attacks in networks. In: 2020 3rd International Conference on Advancements in Computational Sciences (ICACS), pp. 1\u20138 (2020). https:\/\/doi.org\/10.1109\/ICACS47775.2020.9055943. ISBN: VO","DOI":"10.1109\/ICACS47775.2020.9055943"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Spaulding, J., Upadhyaya, S., Mohaisen, A.: The landscape of domain name typosquatting: techniques and countermeasures. In: 2016 11th International Conference on Availability, Reliability and Security (ARES), pp. 284\u2013289 (2016). https:\/\/doi.org\/10.1109\/ARES.2016.84. ISBN: VO","DOI":"10.1109\/ARES.2016.84"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Starov, O., Zhou, Y., Wang, J.: Detecting malicious campaigns in obfuscated JavaScript with scalable behavioral analysis. In: 2019 IEEE Security and Privacy Workshops (SPW), pp. 218\u2013223 (2019). https:\/\/doi.org\/10.1109\/SPW.2019.00048. ISBN: VO","DOI":"10.1109\/SPW.2019.00048"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"Ginsberg, A., Yu, C.: Rapid homoglyph prediction and detection. In: 2018 1st International Conference on Data Intelligence and Security (ICDIS), pp. 17\u201323 (2018). https:\/\/doi.org\/10.1109\/ICDIS.2018.00010. ISBN: VO","DOI":"10.1109\/ICDIS.2018.00010"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Li, X., Geng, G., Yan, Z., Chen, Y., Lee, X.: Phishing detection based on newly registered domains. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3685\u20133692 (2016). https:\/\/doi.org\/10.1109\/BigData.2016.7841036. ISBN: VO","DOI":"10.1109\/BigData.2016.7841036"},{"key":"13_CR13","doi-asserted-by":"publisher","unstructured":"Li, J., Wang, S.: PhishBox: an approach for phishing validation and detection. In: 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC\/PiCom\/DataCom\/CyberSciTech), pp. 557\u2013564 (2017). https:\/\/doi.org\/10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.101. ISBN: VO","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.101"},{"key":"13_CR14","doi-asserted-by":"publisher","unstructured":"Li, Q., Cheng, M., Wang, J., Sun, B.: LSTM based phishing detection for big email data. IEEE Trans. Big Data 1 (2020). https:\/\/doi.org\/10.1109\/TBDATA.2020.2978915. ISSN 2332\u20137790 VO","DOI":"10.1109\/TBDATA.2020.2978915"},{"key":"13_CR15","doi-asserted-by":"publisher","unstructured":"Eshmawi, A., Nair, S.: The roving proxy framewrok for SMS spam and phishing detection. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/CAIS.2019.8769562. ISBN: VO","DOI":"10.1109\/CAIS.2019.8769562"},{"key":"13_CR16","doi-asserted-by":"publisher","unstructured":"Balim, C., Gunal, E.S.: Automatic detection of smishing attacks by machine learning methods. In: 2019 1st International Informatics and Software Engineering Conference (UBMYK), pp. 1\u20133 (2019). https:\/\/doi.org\/10.1109\/UBMYK48245.2019.8965429. ISBN: VO","DOI":"10.1109\/UBMYK48245.2019.8965429"},{"key":"13_CR17","doi-asserted-by":"publisher","unstructured":"Dalgic, F.C., Bozkir, A.S., Aydos, M.: Phish-IRIS: a new approach for vision based brand prediction of phishing web pages via compact visual descriptors. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1\u20138 (2018). https:\/\/doi.org\/10.1109\/ISMSIT.2018.8567299. ISBN: VO","DOI":"10.1109\/ISMSIT.2018.8567299"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Yan, X., Xu, Y., Xing, X., Cui, B., Guo, Z., Guo, T.: Trustworthy network anomaly detection based on an adaptive learning rate and momentum in IIoT. IEEE Trans. Ind. Inform. 1 (2020). https:\/\/doi.org\/10.1109\/TII.2020.2975227. ISSN 1941-0050 VO","DOI":"10.1109\/TII.2020.2975227"},{"key":"13_CR19","doi-asserted-by":"publisher","unstructured":"Sahoo, P.K.: Data mining a way to solve phishing attacks. In: 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), pp. 1\u20135 (2018). https:\/\/doi.org\/10.1109\/ICCTCT.2018.8550910. ISBN: VO","DOI":"10.1109\/ICCTCT.2018.8550910"},{"key":"13_CR20","doi-asserted-by":"publisher","unstructured":"Baykara, M., G\u00fcrel, Z.Z.: Detection of phishing attacks. In: 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pp. 1\u20135 (2018). https:\/\/doi.org\/10.1109\/ISDFS.2018.8355389. ISBN: VO","DOI":"10.1109\/ISDFS.2018.8355389"},{"key":"13_CR21","doi-asserted-by":"publisher","unstructured":"Lingam, G., Rout, R.R., Somayajulu, D.V.L.N.: Detection of social botnet using a trust model based on spam content in Twitter network. In: 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 280\u2013285 (2018). https:\/\/doi.org\/10.1109\/ICIINFS.2018.8721318. ISBN 2164-7011 VO","DOI":"10.1109\/ICIINFS.2018.8721318"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Lingam, G., Rout, R.R., Somayajulu, D.V.L.N.: Deep Q-learning and particle swarm optimization for bot detection in online social networks. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/ICCCNT45670.2019.8944493. ISBN: VO","DOI":"10.1109\/ICCCNT45670.2019.8944493"},{"key":"13_CR23","doi-asserted-by":"publisher","unstructured":"Sharma, H., Meenakshi, E., Bhatia, S.K.: A comparative analysis and awareness survey of phishing detection tools. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1437\u20131442 (2017). https:\/\/doi.org\/10.1109\/RTEICT.2017.8256835. ISBN: VO","DOI":"10.1109\/RTEICT.2017.8256835"},{"key":"13_CR24","doi-asserted-by":"publisher","unstructured":"Pande, D.N., Voditel, P.S.: Spear phishing: diagnosing attack paradigm. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 2720\u20132724 (2017). https:\/\/doi.org\/10.1109\/WiSPNET.2017.8300257. ISBN: VO","DOI":"10.1109\/WiSPNET.2017.8300257"},{"key":"13_CR25","unstructured":"DomainWatch, DomainWatch - Domain WHOIS Search, Website Information. https:\/\/domainwat.ch\/"},{"key":"13_CR26","unstructured":"urlscan, URL and website scanner. https:\/\/urlscan.io\/"},{"key":"13_CR27","doi-asserted-by":"publisher","unstructured":"Zhu, E., Ye, C., Liu, D., Liu, F., Wang, F., Li, X.: An effective neural network phishing detection model based on optimal feature selection. In: 2018 IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom), pp. 781\u2013787 (2018). https:\/\/doi.org\/10.1109\/BDCloud.2018.00117. ISBN: VO","DOI":"10.1109\/BDCloud.2018.00117"},{"key":"13_CR28","doi-asserted-by":"publisher","unstructured":"Yang, P., Zhao, G., Zeng, P.: Phishing website detection based on multidimensional features driven by deep learning. IEEE Access 7, 15 196\u201315 209 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2892066. ISBN: 2169-3536 VO - 7","DOI":"10.1109\/ACCESS.2019.2892066"},{"key":"13_CR29","doi-asserted-by":"publisher","unstructured":"Aung, E.S., Yamana, H.: URL-based phishing detection using the entropy of non-alphanumeric characters. In: Proceedings of the 21st International Conference on Information Integration and Web-Based Applications & Services, iiWAS2019, v. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3366030.3366064. ISBN 9781450371797","DOI":"10.1145\/3366030.3366064"},{"key":"13_CR30","doi-asserted-by":"publisher","unstructured":"McGahagan, J.. Bhansali, ,D, Gratian, M., Cukier, M.: A comprehensive evaluation of HTTP header features for detecting malicious websites. In: 2019 15th European Dependable Computing Conference (EDCC), pp. 75\u201382 (2019). https:\/\/doi.org\/10.1109\/EDCC.2019.00025. ISBN 2641-810X VO","DOI":"10.1109\/EDCC.2019.00025"},{"key":"13_CR31","doi-asserted-by":"publisher","unstructured":"Yuan, H., Chen, X., Li, Y., Yang, Z., Liu, W.: Detecting phishing websites and targets based on URLs and webpage links. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3669\u20133674 (2018). https:\/\/doi.org\/10.1109\/ICPR.2018.8546262. ISBN 1051-4651 VO","DOI":"10.1109\/ICPR.2018.8546262"},{"key":"13_CR32","doi-asserted-by":"publisher","unstructured":"Mondal, S., Maheshwari, D., Pai, N., Biwalkar, A.: A review on detecting phishing URLs using clustering algorithms. In: 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/ICAC347590.2019.9036837. ISBN: VO","DOI":"10.1109\/ICAC347590.2019.9036837"},{"key":"13_CR33","doi-asserted-by":"publisher","unstructured":"Megha, N., Babu, K.R.R., Sherly, E.: An intelligent system for phishing attack detection and prevention. In: 2019 International Conference on Communication and Electronics Systems (ICCES), pp. 1577\u20131582 (2019). https:\/\/doi.org\/10.1109\/ICCES45898.2019.9002204. ISBN: VO","DOI":"10.1109\/ICCES45898.2019.9002204"},{"issue":"6","key":"13_CR34","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1049\/iet-ifs.2019.0006","volume":"13","author":"W Ali","year":"2019","unstructured":"Ali, W., Ahmed, A.A.: Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Inf. Secur. 13(6), 659\u2013669 (2019). https:\/\/doi.org\/10.1049\/iet-ifs.2019.0006. ISSN 1751-8717 VO - 13","journal-title":"IET Inf. Secur."},{"key":"13_CR35","doi-asserted-by":"publisher","unstructured":"Huang, Y., Qin, J., Wen, W.: Phishing URL detection via capsule-based neural network. In: 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), pp. 22\u201326 (2019). https:\/\/doi.org\/10.1109\/ICASID.2019.8925000. ISBN 2163-5056 VO","DOI":"10.1109\/ICASID.2019.8925000"},{"key":"13_CR36","doi-asserted-by":"publisher","unstructured":"Nathezhtha, T., Sangeetha, D., Vaidehi, V.: WC-PAD: web crawling based phishing attack detection. In: 2019 International Carnahan Conference on Security Technology (ICCST), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/CCST.2019.8888416. ISBN 2153-0742 VO","DOI":"10.1109\/CCST.2019.8888416"},{"key":"13_CR37","doi-asserted-by":"publisher","unstructured":"Baral, G., Arachchilage, N.A.G.: Building condence not to be phished through a gamified approach: conceptualising user\u2019s self-efficacy in phishing threat avoidance behaviour. In: 2019 Cybersecurity and Cyberforensics Conference (CCC), pp. 102\u2013110 (2019). https:\/\/doi.org\/10.1109\/CCC.2019.000-1. ISBN: VO","DOI":"10.1109\/CCC.2019.000-1"},{"key":"13_CR38","doi-asserted-by":"publisher","unstructured":"Anand, A., Gorde, K., Moniz, J.R.A., Park, N., Chakraborty, T., Chu, B.: Phishing URL detection with oversampling based on text generative adversarial networks. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1168\u20131177 (2018). https:\/\/doi.org\/10.1109\/BigData.2018.8622547. ISBN: VO","DOI":"10.1109\/BigData.2018.8622547"},{"key":"13_CR39","doi-asserted-by":"publisher","unstructured":"Zuraiq, A.A., Alkasassbeh, M.: Review: phishing detection approaches. In: 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), pp. 1\u20136 (2019). https:\/\/doi.org\/10.1109\/ICTCS.2019.8923069. ISBN: VO","DOI":"10.1109\/ICTCS.2019.8923069"},{"key":"13_CR40","doi-asserted-by":"publisher","unstructured":"Concone, F., Re, G.L., Morana, M., Ruocco, C.: Assisted labeling for spam account detection on Twitter. In: 2019 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 359\u2013366 (2019). https:\/\/doi.org\/10.1109\/SMARTCOMP.2019.00073. ISBN: VO","DOI":"10.1109\/SMARTCOMP.2019.00073"},{"key":"13_CR41","doi-asserted-by":"publisher","unstructured":"Yazhmozhi, V.M., Janet, B.: Natural language processing and machine learning based phishing website detection system. In: 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 336\u2013340 (2019). https:\/\/doi.org\/10.1109\/I-SMAC47947.2019.9032492. ISBN: VO","DOI":"10.1109\/I-SMAC47947.2019.9032492"},{"key":"13_CR42","doi-asserted-by":"publisher","unstructured":"Yao, W., Ding, Y., Li, X.: LogoPhish: a new two-dimensional code phishing attack detection method. In: 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom), pp. 231\u2013236 (2018). https:\/\/doi.org\/10.1109\/BDCloud.2018.00045. ISBN: VO","DOI":"10.1109\/BDCloud.2018.00045"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Xiang, G., Hong, J., Rose, C.P., Cranor, L.: CANTINA+: a featurerich machine learning framework for detecting phishing web sites (2011)","DOI":"10.1145\/2019599.2019606"}],"container-title":["Communications in Computer and Information Science","Applied Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19647-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T21:39:31Z","timestamp":1669153171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19647-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031196461","9783031196478"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19647-8_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Arequipa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai22022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icai.itiud.org","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":"90","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":"32","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":"36% - 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","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)"}}]}}