{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T05:28:35Z","timestamp":1745645315469,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030870485"},{"type":"electronic","value":"9783030870492"}],"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\/978-3-030-87049-2_22","type":"book-chapter","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T05:04:20Z","timestamp":1646283860000},"page":"631-650","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Comparison of\u00a0Performance of\u00a0Rough Set Theory with\u00a0Machine Learning Techniques in\u00a0Detecting Phishing Attack"],"prefix":"10.1007","author":[{"given":"Arpit","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subhas C.","family":"Misra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Abbas, A.R., Singh, S., Kau, M.:. Detection of phishing websites using machine learning. In: Inventive Communication and Computational Technologies, pp. 1307\u20131314. Springer, Singapore (2020)","DOI":"10.1007\/978-981-15-0146-3_128"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Ahmed, K., Naaz, S.: Detection of phishing websites using machine learning approach. In: Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM). Amity University Rajasthan, Jaipur, India, Feb 2019","DOI":"10.2139\/ssrn.3357736"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Alloghani, M., Al-Jumeily, D., Hussain, A., Mustafina, J., Baker, T., ljaaf, A.J.: Implementation of machine learning and data mining to improve cybersecurity and limit vulnerabilities to cyber attacks. In: Nature-Inspired Computation in Data Mining and Machine Learning, pp. 47\u201376. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-28553-1_3"},{"key":"22_CR4","unstructured":"APWG. Accessed on 2 May 2020. https:\/\/docs.apwg.org\/reports\/apwg_trends_report_q3_2019.pdf"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Aydin, M., Butun, I., Bicakci, K., Baykal, N.: Using attribute-based feature selection approaches and machine learning algorithms for detecting fraudulent website URLs. In: 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0774\u20130779. IEEE (2020)","DOI":"10.1109\/CCWC47524.2020.9031125"},{"issue":"12","key":"22_CR6","doi-asserted-by":"publisher","first-page":"4315","DOI":"10.1007\/s00500-018-3084-2","volume":"23","author":"M Babagoli","year":"2019","unstructured":"Babagoli, M., Aghababa, M.P., Solouk, V.: Heuristic nonlinear regression strategy for detecting phishing websites. Soft Comput. 23(12), 4315\u20134327 (2019)","journal-title":"Soft Comput."},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Bujang, M.A., Adnan, T.H.: Requirements for minimum sample size for sensitivity and specificity analysis. J. Clin. Diagn. Res. JCDR 10(10), YE01 (2016)","DOI":"10.7860\/JCDR\/2016\/18129.8744"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Chavan, S., Inamdar, A., Dorle, A., Kulkarni, S., Wu, X.W.: Phishing detection: malicious and benign websites classification using machine learning techniques. In: Proceeding of International Conference on Computational Science and Applications, pp. 437\u2013446. Springer, Singapore (2020)","DOI":"10.1007\/978-981-15-0790-8_43"},{"issue":"6","key":"22_CR9","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.1007\/s00500-016-2443-0","volume":"22","author":"R Cekik","year":"2018","unstructured":"Cekik, R., Telceken, S.: A new classification method based on rough sets theory. Soft Comput. 22(6), 1881\u20131889 (2018)","journal-title":"Soft Comput."},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.ins.2019.01.064","volume":"484","author":"KL Chiew","year":"2019","unstructured":"Chiew, K.L., Tan, C.L., Wong, K., Yong, K.S., Tiong, W.K.: A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf. Sci. 484, 153\u2013166 (2019)","journal-title":"Inf. Sci."},{"key":"22_CR11","unstructured":"CSO. Accessed on 1 May 2020. https:\/\/www.csoonline.com\/article\/2117843\/what-is-phishing-how-this-cyber-attack-works-and-how-to-prevent-it.html"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Cuzzocrea, A., Martinelli, F., Mercaldo, F.: A machine-learning framework for supporting intelligent web-phishing detection and analysis. In: Proceedings of the 23rd International Database Applications and Engineering Symposium, pp. 1\u20133, June 2019","DOI":"10.1145\/3331076.3331087"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233\u2013240, June 2006","DOI":"10.1145\/1143844.1143874"},{"issue":"2","key":"22_CR14","first-page":"1","volume":"9","author":"K Demertzis","year":"2019","unstructured":"Demertzis, K., Iliadis, L.: Cognitive web application firewall to critical infrastructures protection from phishing attacks. J. Comput. Modell. 9(2), 1\u201326 (2019)","journal-title":"J. Comput. Modell."},{"issue":"2","key":"22_CR15","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1093\/bib\/bbz016","volume":"21","author":"JJ Dziak","year":"2020","unstructured":"Dziak, J.J., Coffman, D.L., Lanza, S.T., Li, R., Jermiin, L.S.: Sensitivity and specificity of information criteria. Brief. Bioinform. 21(2), 553\u2013565 (2020)","journal-title":"Brief. Bioinform."},{"key":"22_CR16","unstructured":"Eralda, H., Vaes, B., Van Pottelbergh, G., Mathe\u00ef, C., Verbakel, J., Degryse, J.M.: Predictive accuracy of frailty tools for adverse outcomes in a cohort of adults 80 years and older: a decision curve analysis. J. Am. Med. Dir. Assoc. (2019)"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Greco, S., Matarazzo, B., Slowinski, R., Stefanowski, J.: Variable consistency model of dominance-based rough sets approach. In: International Conference on Rough Sets and Current Trends in Computing, pp. 170\u2013181. Springer, Berlin, Heidelberg (2000)","DOI":"10.1007\/3-540-45554-X_20"},{"issue":"1","key":"22_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0377-2217(00)00167-3","volume":"129","author":"S Greco","year":"2001","unstructured":"Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129(1), 1\u201347 (2001)","journal-title":"Eur. J. Oper. Res."},{"key":"22_CR19","unstructured":"Hindustan times. Accessed on 1 May 2020. https:\/\/www.hindustantimes.com\/delhi\/pakistan-group-hacks-iit-delhi-du-websites-posts-about-kashmir-violence\/story-lsQ8Q08ksLfsRMoA0gcaEO.html"},{"key":"22_CR20","unstructured":"Imperva. Accessed on 1 May 2020. https:\/\/www.imperva.com\/learn\/application-security\/phishing-attack-scam\/"},{"issue":"5","key":"22_CR21","doi-asserted-by":"publisher","first-page":"2015","DOI":"10.1007\/s12652-018-0798-z","volume":"10","author":"AK Jain","year":"2019","unstructured":"Jain, A.K., Gupta, B.B.: A machine learning based approach for phishing detection using hyperlinks information. J. Am. Intell. Human. Comput. 10(5), 2015\u20132028 (2019)","journal-title":"J. Am. Intell. Human. Comput."},{"key":"22_CR22","volume-title":"Logistic Regression","author":"DG Kleinbaum","year":"2002","unstructured":"Kleinbaum, D.G., Dietz, K., Gail, M., Klein, M., Klein, M.: Logistic Regression. Springer, New York (2002)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Kulkarni, A.: Phishing Websites Detection Using Machine Learning (2019)","DOI":"10.14569\/IJACSA.2019.0100702"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Le Pochat, V., Van Goethem, T., Joosen, W.:. Funny accents: exploring genuine interest in internationalized domain names. In: International Conference on Passive and Active Network Measurement, pp. 178\u2013194. Springer, Cham (2019)","DOI":"10.1007\/978-3-030-15986-3_12"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Li, Y., Xiong, K., Li, X.: Understanding user behaviors when phishing attacks occur. In: 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), p. 222. IEEE (2019)","DOI":"10.1109\/ISI.2019.8823468"},{"issue":"3","key":"22_CR26","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A., Wiener, M.: Classification and regression by RandomForest. R News 2(3), 18\u201322 (2002)","journal-title":"R News"},{"key":"22_CR27","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.inffus.2019.01.001","volume":"49","author":"K Lv","year":"2019","unstructured":"Lv, K., Chen, Y., Hu, C.: Dynamic defense strategy against advanced persistent threat under heterogeneous networks. Inform. Fusion 49, 216\u2013226 (2019)","journal-title":"Inform. Fusion"},{"key":"22_CR28","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.eswa.2016.01.028","volume":"53","author":"M Moghimi","year":"2016","unstructured":"Moghimi, M., Varjani, A.Y.: New rule-based phishing detection method. Exp. Syst. Appl. 53, 231\u2013242 (2016)","journal-title":"Exp. Syst. Appl."},{"key":"22_CR29","unstructured":"Mohammad, R.M., Thabtah, F., McCluskey, L.: An assessment of features related to phishing websites using an automated technique. In: 2012 International Conference for Internet Technology and Secured Transactions, pp. 492\u2013497. IEEE (2012)"},{"issue":"9","key":"22_CR30","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1109\/TKDE.2017.2682249","volume":"29","author":"M Ohsaki","year":"2017","unstructured":"Ohsaki, M., Wang, P., Matsuda, K., Katagiri, S., Watanabe, H., Ralescu, A.: Confusion-matrix-based kernel logistic regression for imbalanced data classification. IEEE Trans. Knowl. Data Eng. 29(9), 1806\u20131819 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"22_CR31","unstructured":"Orunsolu, A.A., Sodiya, A.S., Akinwale, A.T.: A predictive model for phishing detection. J. King Saud Univ. Comput. Inf. Sci. (2019)"},{"key":"22_CR32","doi-asserted-by":"publisher","unstructured":"Pawlak, Z.: Vagueness a rough set view In: Mycielski, J., Rozenberg, G., Salomaa, A. (eds.) Structures in Logic and Computer Science, vol. 1261, pp. 106\u2013117. Springer, Berlin, Heidelberg (1997). https:\/\/doi.org\/10.1007\/3-540-63246-8_7","DOI":"10.1007\/3-540-63246-8_7"},{"issue":"1","key":"22_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.ins.2006.06.003","volume":"177","author":"Z Pawlak","year":"2007","unstructured":"Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3\u201327 (2007)","journal-title":"Inf. Sci."},{"key":"22_CR34","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.eswa.2017.12.020","volume":"97","author":"I Portugal","year":"2018","unstructured":"Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review. Exp. Syst. Appl. 97, 205\u2013227 (2018)","journal-title":"Exp. Syst. Appl."},{"key":"22_CR35","unstructured":"Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)"},{"key":"22_CR36","doi-asserted-by":"crossref","unstructured":"Prasad, R., Rohokale, V.: Phishing. In: Cyber Security: The Lifeline of Information and Communication Technology, pp. 33\u201342. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-31703-4_3"},{"key":"22_CR37","doi-asserted-by":"crossref","unstructured":"Rajab, M.: An anti-phishing method based on feature analysis. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, pp. 133\u2013139, Feb 2018","DOI":"10.1145\/3184066.3184082"},{"issue":"8","key":"22_CR38","doi-asserted-by":"publisher","first-page":"3851","DOI":"10.1007\/s00521-017-3305-0","volume":"31","author":"RS Rao","year":"2019","unstructured":"Rao, R.S., Pais, A.R.: Detection of phishing websites using an efficient feature-based machine learning framework. Neural Comput. Appl. 31(8), 3851\u20133873 (2019)","journal-title":"Neural Comput. Appl."},{"key":"22_CR39","unstructured":"Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3(22), pp. 41\u201346 (2001)"},{"key":"22_CR40","doi-asserted-by":"crossref","unstructured":"Siahaan, H., Mawengkang, H., Efendi, S., Wanto, A., Windarto, A.P.: Application of classification method C4.5 on selection of exemplary teachers. J. Phys. Conf. Ser. 1235(1), 012005 (2019)","DOI":"10.1088\/1742-6596\/1235\/1\/012005"},{"key":"22_CR41","doi-asserted-by":"publisher","first-page":"104702","DOI":"10.1016\/j.ssci.2020.104702","volume":"127","author":"A Singh","year":"2020","unstructured":"Singh, A., Misra, S.C.: A dominance based Rough set analysis for investigating employee perception of safety at workplace and safety compliance. Saf. Sci. 127, 104702 (2020)","journal-title":"Saf. Sci."},{"key":"22_CR42","doi-asserted-by":"crossref","unstructured":"S\u0142owi\u0144ski, R., Greco, S., Matarazzo, B.: Rough set analysis of preference-ordered data. In: International Conference on Rough Sets and Current Trends in Computing, pp. 44\u201359. Springer, Berlin, Heidelberg (2002)","DOI":"10.1007\/3-540-45813-1_6"},{"issue":"3","key":"22_CR43","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293\u2013300 (1999)","journal-title":"Neural Process. Lett."},{"key":"22_CR44","unstructured":"UCI repository. Accessed on 3 May 2020. https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/00327\/"},{"key":"22_CR45","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.chb.2019.07.034","volume":"101","author":"SF Verkijika","year":"2019","unstructured":"Verkijika, S.F.: \u201cIf you know what to do, will you take action to avoid mobile phishing attacks\u2019\u2019: self-efficacy, anticipated regret, and gender. Comput. Hum. Behav. 101, 286\u2013296 (2019)","journal-title":"Comput. Hum. Behav."},{"key":"22_CR46","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.ins.2019.05.085","volume":"501","author":"S Vluymans","year":"2019","unstructured":"Vluymans, S., Mac Parthalain, N., Cornelis, C., Saeys, Y.: Weight selection strategies for ordered weighted average based fuzzy rough sets. Inform. Sci. 501, 155\u2013171 (2019)","journal-title":"Inform. Sci."},{"key":"22_CR47","doi-asserted-by":"crossref","unstructured":"Wei, W., Miao, D., Li, Y.: A bibliometric profile of research on rough sets. In: International Joint Conference on Rough Sets, pp. 534\u2013548. Springer, Cham (2019)","DOI":"10.1007\/978-3-030-22815-6_41"},{"key":"22_CR48","doi-asserted-by":"crossref","unstructured":"Zamir, A., Khan, H.U., Iqbal, T., Yousaf, N., Aslam, F., Anjum, A., Hamdani, M.: Phishing web site detection using diverse machine learning algorithms. Electron. Libr. (2020)","DOI":"10.1108\/EL-05-2019-0118"}],"container-title":["Lecture Notes in Networks and Systems","Advances in Computing, Informatics, Networking and Cybersecurity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87049-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T12:14:25Z","timestamp":1651148065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87049-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030870485","9783030870492"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87049-2_22","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}