{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:04:57Z","timestamp":1767373497869,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11042-021-11073-x","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T08:02:51Z","timestamp":1624262571000},"page":"9459-9479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A RAkEL-based methodology to estimate software vulnerability characteristics & score - an application to EU project ECHO"],"prefix":"10.1007","volume":"81","author":[{"given":"Georgios","family":"Aivatoglou","sequence":"first","affiliation":[]},{"given":"Mike","family":"Anastasiadis","sequence":"additional","affiliation":[]},{"given":"Georgios","family":"Spanos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4012-8511","authenticated-orcid":false,"given":"Antonis","family":"Voulgaridis","sequence":"additional","affiliation":[]},{"given":"Konstantinos","family":"Votis","sequence":"additional","affiliation":[]},{"given":"Dimitrios","family":"Tzovaras","sequence":"additional","affiliation":[]},{"given":"Lefteris","family":"Angelis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"11073_CR1","unstructured":"Bodungen C (2019) Industrial vulnerability scoring system (ivss). https:\/\/securingics.com\/IVSS\/IVSS.html"},{"key":"11073_CR2","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.dss.2015.11.003","volume":"82","author":"M Bogaert","year":"2016","unstructured":"Bogaert M, Ballings M, Van den Poel D (2016) The added value of facebook friends data in event attendance prediction. Decis Support Syst 82:26\u201334","journal-title":"Decis Support Syst"},{"issue":"9","key":"11073_CR3","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell M R, Luo J, Shen X, Brown C M (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757\u20131771","journal-title":"Pattern Recogn"},{"key":"11073_CR4","unstructured":"Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman & Hall, New York"},{"issue":"2","key":"11073_CR5","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"1","key":"11073_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"11073_CR7","doi-asserted-by":"crossref","unstructured":"Chen J, Kudjo P K, Mensah S, Brown S A, Akorfu G (2020) An automatic software vulnerability classification framework using term frequency-inverse gravity moment and feature selection. J Syst Softw:110616","DOI":"10.1016\/j.jss.2020.110616"},{"issue":"1-2","key":"11073_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-012-5285-8","volume":"88","author":"K Dembczy\u0144ski","year":"2012","unstructured":"Dembczy\u0144ski K, Waegeman W, Cheng W, H\u00fcllermeier E (2012) On label dependence and loss minimization in multi-label classification. Mach Learn 88(1-2):5\u201345","journal-title":"Mach Learn"},{"key":"11073_CR9","unstructured":"First O (2015) Common vulnerability scoring system v3.0: user guide. https:\/\/www.first.org\/cvss\/cvss-v30-user_guide_v1.4.pdf"},{"issue":"771\u2013780","key":"11073_CR10","first-page":"1612","volume":"14","author":"Y Freund","year":"1999","unstructured":"Freund Y, Schapire R, Abe N (1999) A short introduction to boosting. J-Japan Soc Artif Intell 14(771\u2013780):1612","journal-title":"J-Japan Soc Artif Intell"},{"issue":"8","key":"11073_CR11","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho T K (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832\u2013844","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11073_CR12","doi-asserted-by":"publisher","first-page":"28291","DOI":"10.1109\/ACCESS.2019.2900462","volume":"7","author":"G Huang","year":"2019","unstructured":"Huang G, Li Y, Wang Q, Ren J, Cheng Y, Zhao X (2019) Automatic classification method for software vulnerability based on deep neural network. IEEE Access 7:28291\u201328298","journal-title":"IEEE Access"},{"issue":"4","key":"11073_CR13","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","volume":"22","author":"RJ Hyndman","year":"2006","unstructured":"Hyndman R J, Koehler A B (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679\u2013688","journal-title":"Int J Forecast"},{"key":"11073_CR14","doi-asserted-by":"crossref","unstructured":"Kudjo P K, Chen J, Mensah S, Amankwah R, Kudjo C (2020) The effect of bellwether analysis on software vulnerability severity prediction models. Softw Qual J:1\u201334","DOI":"10.1007\/s11219-019-09490-1"},{"key":"11073_CR15","doi-asserted-by":"crossref","unstructured":"Kudjo P K, Chen J, Zhou M, Mensah S, Huang R (2019) Improving the accuracy of vulnerability report classification using term frequency-inverse gravity moment. In: 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS). IEEE, pp 248\u2013259","DOI":"10.1109\/QRS.2019.00041"},{"key":"11073_CR16","doi-asserted-by":"crossref","unstructured":"Le T H M, Sabir B, Babar M A (2019) Automated software vulnerability assessment with concept dr ift. In: 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR). IEEE, pp 371\u2013382","DOI":"10.1109\/MSR.2019.00063"},{"issue":"3","key":"11073_CR17","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M, et al. (2002) Classification and regression by randomforest. R news 2(3):18\u201322","journal-title":"R news"},{"key":"11073_CR18","doi-asserted-by":"crossref","unstructured":"Liu C, Li J, Chen X (2012) Network vulnerability analysis using text mining. In: Asian Conference on Intelligent Information and Database Systems. Springer, pp 274\u2013283","DOI":"10.1007\/978-3-642-28490-8_29"},{"key":"11073_CR19","doi-asserted-by":"crossref","unstructured":"Liu K, Zhou Y, Wang Q, Zhu X (2019) Vulnerability severity prediction with deep neural network. In: 2019 5th International Conference on Big Data and Information Analytics (BigDIA). IEEE, pp 114\u2013119","DOI":"10.1109\/BigDIA.2019.8802851"},{"issue":"3","key":"11073_CR20","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.comcom.2010.04.006","volume":"34","author":"Q Liu","year":"2011","unstructured":"Liu Q, Zhang Y (2011) Vrss: A new system for rating and scoring vulnerabilities. Comput Commun 34(3):264\u2013273","journal-title":"Comput Commun"},{"issue":"8","key":"11073_CR21","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1016\/j.jss.2012.03.057","volume":"85","author":"Q Liu","year":"2012","unstructured":"Liu Q, Zhang Y, Kong Y, Wu Q (2012) Improving vrss-based vulnerability prioritization using analytic hierarchy process. J Syst Softw 85(8):1699\u20131708","journal-title":"J Syst Softw"},{"key":"11073_CR22","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.dss.2016.06.013","volume":"89","author":"M Meire","year":"2016","unstructured":"Meire M, Ballings M, Van den Poel D (2016) The added value of auxiliary data in sentiment analysis of facebook posts. Decis Support Syst 89:98\u2013112","journal-title":"Decis Support Syst"},{"key":"11073_CR23","doi-asserted-by":"crossref","unstructured":"Mell P, Scarfone K, Romanosky S (2007) A complete guide to the common vulnerability scoring system version 2.0 1, 23","DOI":"10.1049\/iet-ifs:20060055"},{"key":"11073_CR24","doi-asserted-by":"crossref","unstructured":"Na S, Kim T, Kim H (2016) A study on the classification of common vulnerabilities and exposures using na\u00efve bayes. In: International Conference on Broadband and Wireless Computing, Communication and Applications. Springer, pp 657\u2013662","DOI":"10.1007\/978-3-319-49106-6_65"},{"key":"11073_CR25","doi-asserted-by":"crossref","unstructured":"Neuhaus S, Zimmermann T (2010) Security trend analysis with cve topic models. In: 2010 IEEE 21st International Symposium on Software Reliability Engineering. IEEE, pp 111\u2013120","DOI":"10.1109\/ISSRE.2010.53"},{"key":"11073_CR26","unstructured":"Quinlan J (2014) C4. 5: programs for machine learning. Elsevier"},{"issue":"2","key":"11073_CR27","first-page":"129","volume":"15","author":"J Ruohonen","year":"2019","unstructured":"Ruohonen J (2019) A look at the time delays in cvss vulnerability scoring. Appl Comput Inf 15(2):129\u2013135","journal-title":"Appl Comput Inf"},{"key":"11073_CR28","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.jss.2019.06.001","volume":"156","author":"ER Russo","year":"2019","unstructured":"Russo E R, Di Sorbo A, Visaggio C A, Canfora G (2019) Summarizing vulnerabilities\u2019 descriptions to support experts during vulnerability assessment activities. J Syst Softw 156:84\u201399","journal-title":"J Syst Softw"},{"key":"11073_CR29","doi-asserted-by":"crossref","unstructured":"Sahin S E, Tosun A (2019) A conceptual replication on predicting the severity of software vulnerabilities. In: Proceedings of the Evaluation and Assessment on Software Engineering, pp 244\u2013250","DOI":"10.1145\/3319008.3319033"},{"key":"11073_CR30","unstructured":"Schiffman M, Cisco CIAG (2005) A complete guide to the common vulnerability scoring system (cvss). White paper. Identification of Basic Measurable Security Components in Software Intensive Systems"},{"issue":"1-3","key":"11073_CR31","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1080\/19393555.2015.1051675","volume":"24","author":"G Spanos","year":"2015","unstructured":"Spanos G, Angelis L (2015) Impact metrics of security vulnerabilities: Analysis and weighing. Inf Secur J Glob Perspect 24(1-3):57\u201371","journal-title":"Inf Secur J Glob Perspect"},{"key":"11073_CR32","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jss.2018.09.039","volume":"146","author":"G Spanos","year":"2018","unstructured":"Spanos G, Angelis L (2018) A multi-target approach to estimate software vulnerability characteristics and severity scores. J Syst Softw 146:152\u2013166","journal-title":"J Syst Softw"},{"key":"11073_CR33","doi-asserted-by":"crossref","unstructured":"Spanos G, Angelis L, Toloudis D (2017) Assessment of vulnerability severity using text mining. In: Proceedings of the 21st Pan-Hellenic Conference on Informatics, pp 1\u20136","DOI":"10.1145\/3139367.3139390"},{"key":"11073_CR34","doi-asserted-by":"crossref","unstructured":"Spanos G, Sioziou A, Angelis L (2013) Wivss: a new methodology for scoring information systems vulnerabilities. In: Proceedings of the 17th Panhellenic Conference on Informatics, pp 83\u201390","DOI":"10.1145\/2491845.2491871"},{"key":"11073_CR35","doi-asserted-by":"crossref","unstructured":"Toloudis D, Spanos G, Angelis L (2016) Associating the severity of vulnerabilities with their description. In: International Conference on Advanced Information Systems Engineering. Springer, pp 231\u2013242","DOI":"10.1007\/978-3-319-39564-7_22"},{"issue":"7","key":"11073_CR36","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1109\/TKDE.2010.164","volume":"23","author":"G Tsoumakas","year":"2010","unstructured":"Tsoumakas G, Katakis I, Vlahavas I (2010) Random k-labelsets for multilabel classification. IEEE Trans Knowl Data Eng 23(7):1079\u20131089","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"11073_CR37","first-page":"579","volume":"8","author":"Y Wang","year":"2012","unstructured":"Wang Y, Yang Y (2012) Pvl: a novel metric for single vulnerability rating and its application in ims. J Comput Inf Syst 8(2):579\u2013590","journal-title":"J Comput Inf Syst"},{"key":"11073_CR38","doi-asserted-by":"crossref","unstructured":"Yamamoto Y, Miyamoto D, Nakayama M (2015) Text-mining approach for estimating vulnerability score. In: 2015 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS). IEEE, pp 67\u201373","DOI":"10.1109\/BADGERS.2015.018"},{"issue":"8","key":"11073_CR39","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M-L Zhang","year":"2013","unstructured":"Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11073-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11073-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11073-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T17:20:59Z","timestamp":1648056059000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11073-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["11073"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11073-x","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,6,21]]},"assertion":[{"value":"30 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}