{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T21:43:47Z","timestamp":1774129427264,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"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":["Wireless Pers Commun"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11277-022-09576-3","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T07:03:16Z","timestamp":1645599796000},"page":"755-784","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["PaSOFuAC: Particle Swarm Optimization Based Fuzzy Associative Classifier for Detecting Phishing Websites"],"prefix":"10.1007","volume":"125","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9377-5689","authenticated-orcid":false,"given":"S.","family":"Priya","sequence":"first","affiliation":[]},{"given":"S.","family":"Selvakumar","sequence":"additional","affiliation":[]},{"given":"R. Leela","family":"velusamy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"issue":"8","key":"9576_CR1","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. (2019). Detection of phishing websites using an efficient feature-based machine learning framework. Neural Computing and Applications, 31(8), 3851\u20133873.","journal-title":"Neural Computing and Applications"},{"key":"9576_CR2","unstructured":"APWG Report. (2019). Retrieved December 2019 from https:\/\/docs.apwg.org\/reports\/apwg_trends_report_q3_2019.pdf"},{"key":"9576_CR3","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1016\/j.future.2017.11.018","volume":"82","author":"C Zou","year":"2018","unstructured":"Zou, C., Deng, H., Wan, J., Wang, Z., & Deng, P. (2018). Mining and updating association rules based on fuzzy concept lattice. Future Generation Computer Systems, 82, 698\u2013706.","journal-title":"Future Generation Computer Systems"},{"key":"9576_CR4","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1016\/j.asoc.2016.08.005","volume":"48","author":"W Hadi","year":"2016","unstructured":"Hadi, W., Aburub, F., & Alhawari, S. (2016). A new fast associative classification algorithm for detecting phishing websites. Applied Soft Computing, 48, 729\u2013734.","journal-title":"Applied Soft Computing"},{"issue":"13","key":"9576_CR5","doi-asserted-by":"publisher","first-page":"5948","DOI":"10.1016\/j.eswa.2014.03.019","volume":"41","author":"N Abdelhamid","year":"2014","unstructured":"Abdelhamid, N., Ayesh, A., & Thabtah, F. (2014). Phishing detection based associative classification data mining. Expert Systems with Applications, 41(13), 5948\u20135959.","journal-title":"Expert Systems with Applications"},{"issue":"8","key":"9576_CR6","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1016\/j.ins.2009.12.020","volume":"180","author":"FJ Berlanga","year":"2010","unstructured":"Berlanga, F. J., Rivera, A. J., del Jes\u00fas, M. J., & Herrera, F. (2010). Gp-coach: Genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems. Information Sciences, 180(8), 1183\u20131200.","journal-title":"Information Sciences"},{"issue":"6","key":"9576_CR7","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1080\/18756891.2014.967008","volume":"7","author":"D Garc\u00eda","year":"2014","unstructured":"Garc\u00eda, D., Gonz\u00e1lez, A., & P\u00e9rez, R. (2014). Overview of the slave learning algorithm: A review of its evolution and prospects. International Journal of Computational Intelligence Systems, 7(6), 1194\u20131221.","journal-title":"International Journal of Computational Intelligence Systems"},{"key":"9576_CR8","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.knosys.2016.10.027","volume":"116","author":"JM-T Wu","year":"2017","unstructured":"Wu, J.M.-T., Zhan, J., & Lin, J.C.-W. (2017). An aco-based approach to mine high-utility itemsets. Knowledge-Based Systems, 116, 102\u2013113.","journal-title":"Knowledge-Based Systems"},{"key":"9576_CR9","unstructured":"Tan, C. L. (2018). Phishing dataset for machine learning: Feature evaluation, mendeley data, v1. Retrieved October 2019 from https:\/\/doi.org\/10.17632\/h3cgnj8hft.1"},{"key":"9576_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.dss.2016.05.005","volume":"88","author":"CL Tan","year":"2016","unstructured":"Tan, C. L., Chiew, K. L., Wong, K. S., et al. (2016). Phishwho: Phishing webpage detection via identity keywords extraction and target domain name finder. Decision Support Systems, 88, 18\u201327.","journal-title":"Decision Support Systems"},{"key":"9576_CR11","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. (2016). New rule-based phishing detection method. Expert Systems with Applications, 53, 231\u2013242.","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"9576_CR12","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/TNSM.2014.2377295","volume":"11","author":"S Marchal","year":"2014","unstructured":"Marchal, S., Fran\u00e7ois, J., State, R., & Engel, T. (2014). Phishstorm: Detecting phishing with streaming analytics. IEEE Transactions on Network and Service Management, 11(4), 458\u2013471.","journal-title":"IEEE Transactions on Network and Service Management"},{"issue":"1","key":"9576_CR13","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s13673-017-0098-1","volume":"7","author":"M Zouina","year":"2017","unstructured":"Zouina, M., & Outtaj, B. (2017). A novel lightweight URL phishing detection system using SVM and similarity index. Human-centric Computing and Information Sciences, 7(1), 17.","journal-title":"Human-centric Computing and Information Sciences"},{"issue":"2","key":"9576_CR14","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s00521-013-1490-z","volume":"25","author":"RM Mohammad","year":"2014","unstructured":"Mohammad, R. M., Thabtah, F., & McCluskey, L. (2014). Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25(2), 443\u2013458.","journal-title":"Neural Computing and Applications"},{"key":"9576_CR15","doi-asserted-by":"crossref","unstructured":"Gupta, S., & Singhal, A. (2017). Phishing URL detection by using artificial neural network with PSO. In 2017 2nd International Conference on Telecommunication and Networks (TEL-NET) (pp. 1\u20136). IEEE.","DOI":"10.1109\/TEL-NET.2017.8343553"},{"issue":"1","key":"9576_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13673-016-0064-3","volume":"6","author":"SC Jeeva","year":"2016","unstructured":"Jeeva, S. C., & Rajsingh, E. B. (2016). Intelligent phishing URL detection using association rule mining. Human-centric Computing and Information Sciences, 6(1), 1\u201319.","journal-title":"Human-centric Computing and Information Sciences"},{"issue":"1","key":"9576_CR17","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.aci.2014.07.002","volume":"11","author":"N Abdelhamid","year":"2015","unstructured":"Abdelhamid, N. (2015). Multi-label rules for phishing classification. Applied Computing and Informatics, 11(1), 29\u201346.","journal-title":"Applied Computing and Informatics"},{"key":"9576_CR18","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/j.asoc.2017.05.050","volume":"67","author":"M Elkano","year":"2018","unstructured":"Elkano, M., Galar, M., Sanz, J. A., Schiavo, P. F., Pereira, S., Jr., Dimuro, G. P., et al. (2018). Consensus via penalty functions for decision making in ensembles in fuzzy rule-based classification systems. Applied Soft Computing, 67, 728\u2013740.","journal-title":"Applied Soft Computing"},{"issue":"2\u20133","key":"9576_CR19","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2\u20133), 191\u2013203.","journal-title":"Computers & Geosciences"},{"issue":"10","key":"9576_CR20","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s00500-013-1023-9","volume":"17","author":"S Yue","year":"2013","unstructured":"Yue, S., Wang, P., Wang, J., & Huang, T. (2013). Extension of the gap statistics index to fuzzy clustering. Soft Computing, 17(10), 1833\u20131846.","journal-title":"Soft Computing"},{"issue":"2","key":"9576_CR21","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","volume":"36","author":"A Likas","year":"2003","unstructured":"Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451\u2013461.","journal-title":"Pattern Recognition"},{"issue":"3","key":"9576_CR22","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1109\/TFUZZ.2011.2106216","volume":"19","author":"H Le Capitaine","year":"2011","unstructured":"Le Capitaine, H., & Frelicot, C. (2011). A cluster-validity index combining an overlap measure and a separation measure based on fuzzy-aggregation operators. IEEE Transactions on Fuzzy Systems, 19(3), 580\u2013588.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"9576_CR23","unstructured":"\u015eEN\u00d6Z, E.\u00a0R. (2019). Evaluation of the robustness performance of a fuzzy logic controller for active vibration control of a piezo-beam via tip mass location variation. PhD thesis, Middle East Technical University."},{"key":"9576_CR24","doi-asserted-by":"crossref","unstructured":"Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN\u201995-International Conference on Neural Networks, (vol.\u00a04, pp. 1942\u20131948). IEEE.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"9576_CR25","doi-asserted-by":"crossref","unstructured":"Cervantes, A., Galvan, I., & Isasi, P. (2005). A comparison between the Pittsburgh and Michigan approaches for the binary PSO algorithm. In 2005 IEEE Congress on Evolutionary Computation, (vol.\u00a01, pp. 290\u2013297). IEEE.","DOI":"10.1109\/CEC.2005.1554697"},{"issue":"2","key":"9576_CR26","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/TAP.2004.823969","volume":"52","author":"J Robinson","year":"2004","unstructured":"Robinson, J., & Rahmat-Samii, Y. (2004). Particle swarm optimization in electromagnetics. IEEE Transactions on Antennas and Propagation, 52(2), 397\u2013407.","journal-title":"IEEE Transactions on Antennas and Propagation"},{"issue":"6","key":"9576_CR27","doi-asserted-by":"publisher","first-page":"3067","DOI":"10.3233\/IFS-152034","volume":"30","author":"AA Afify","year":"2016","unstructured":"Afify, A. A. (2016). A fuzzy rule induction algorithm for discovering classification rules. Journal of Intelligent & Fuzzy Systems, 30(6), 3067\u20133085.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"9576_CR28","first-page":"80","volume":"98","author":"B Liu","year":"1998","unstructured":"Liu, B., Hsu, W., Ma, Y., et al. (1998). Integrating classification and association rule mining. KDD, 98, 80\u201386.","journal-title":"KDD"},{"issue":"5","key":"9576_CR29","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1109\/TFUZZ.2011.2147794","volume":"19","author":"J Alcala-Fdez","year":"2011","unstructured":"Alcala-Fdez, J., Alcala, R., & Herrera, F. (2011). A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Transactions on Fuzzy Systems, 19(5), 857\u2013872.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"7","key":"9576_CR30","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1080\/08839510600779688","volume":"20","author":"B Kav\u0161ek","year":"2006","unstructured":"Kav\u0161ek, B., & Lavra\u010d, N. (2006). Apriori-sd: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence, 20(7), 543\u2013583.","journal-title":"Applied Artificial Intelligence"},{"key":"9576_CR31","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.asoc.2017.11.013","volume":"62","author":"J Alwidian","year":"2018","unstructured":"Alwidian, J., Hammo, B. H., & Obeid, N. (2018). Wcba: Weighted classification based on association rules algorithm for breast cancer disease. Applied Soft Computing, 62, 536\u2013549.","journal-title":"Applied Soft Computing"},{"key":"9576_CR32","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.ins.2017.07.025","volume":"417","author":"W Hadi","year":"2017","unstructured":"Hadi, W., Issa, G., & Ishtaiwi, A. (2017). Acprism: Associative classification based on prism algorithm. Information Sciences, 417, 287\u2013300.","journal-title":"Information Sciences"},{"key":"9576_CR33","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. S., Yong, K. S. C., & Tiong, W. K. (2019). A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Information Sciences, 484, 153\u2013166.","journal-title":"Information Sciences"},{"key":"9576_CR34","doi-asserted-by":"crossref","unstructured":"Zabihimayvan, M., & Doran, D. (2019). Fuzzy rough set feature selection to enhance phishing attack detection. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1\u20136). IEEE.","DOI":"10.1109\/FUZZ-IEEE.2019.8858884"},{"key":"9576_CR35","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/978-1-4615-1733-7_30","volume-title":"Data mining for scientific and engineering applications","author":"B Liu","year":"2001","unstructured":"Liu, B., Ma, Y., & Wong, C.-K. (2001). Classification using association rules: weaknesses and enhancements. In R. L. Grossman (Ed.), Data mining for scientific and engineering applications (pp. 591\u2013605). Springer."},{"key":"9576_CR36","unstructured":"Li, W., Han, J., & Pei, J. (2001). Cmar: Accurate and efficient classification based on multiple class-association rules. In Proceedings 2001 IEEE International Conference on Data Mining (pp. 369\u2013376.) IEEE."},{"key":"9576_CR37","doi-asserted-by":"crossref","unstructured":"Yin, X., & Han, J. (2003). Cpar: Classification based on predictive association rules. In Proceedings of the 2003 SIAM International Conference on Data Mining (pp. 331\u2013335). SIAM.","DOI":"10.1137\/1.9781611972733.40"},{"key":"9576_CR38","first-page":"79","volume":"20","author":"X-S Yang","year":"2008","unstructured":"Yang, X.-S., et al. (2008). Firefly algorithm. Nature-Inspired Metaheuristic Algorithms, 20, 79\u201390.","journal-title":"Nature-Inspired Metaheuristic Algorithms"},{"issue":"2","key":"9576_CR39","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/TEVC.2008.927706","volume":"13","author":"AK Qin","year":"2008","unstructured":"Qin, A. K., Huang, V. L., & Suganthan, P. N. (2008). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, 13(2), 398\u2013417.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"9576_CR40","unstructured":"KEEL Repository. (2019). Retrieved October 2019 from http:\/\/sci2s.ugr.es\/keel\/datasets.php"},{"issue":"3","key":"9576_CR41","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/3477.931534","volume":"31","author":"A Gonz\u00e1lez","year":"2001","unstructured":"Gonz\u00e1lez, A., & P\u00e9rez, R. (2001). Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31(3), 417\u2013425.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"issue":"4","key":"9576_CR42","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1109\/TFUZZ.2008.915790","volume":"16","author":"EG Mansoori","year":"2008","unstructured":"Mansoori, E. G., Zolghadri, M. J., & Katebi, S. D. (2008). Sgerd: A steady-state genetic algorithm for extracting fuzzy classification rules from data. IEEE Transactions on Fuzzy Systems, 16(4), 1061\u20131071.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"4","key":"9576_CR43","doi-asserted-by":"publisher","first-page":"2086","DOI":"10.1016\/j.eswa.2014.09.021","volume":"42","author":"M Antonelli","year":"2015","unstructured":"Antonelli, M., Ducange, P., Marcelloni, F., & Segatori, A. (2015). A novel associative classification model based on a fuzzy frequent pattern mining algorithm. Expert Systems with Applications, 42(4), 2086\u20132097.","journal-title":"Expert Systems with Applications"},{"issue":"12","key":"9576_CR44","doi-asserted-by":"publisher","first-page":"4577","DOI":"10.1007\/s10489-018-1224-0","volume":"48","author":"IB Slima","year":"2018","unstructured":"Slima, I. B., & Borgi, A. (2018). Supervised methods for regrouping attributes in fuzzy rule-based classification systems. Applied Intelligence, 48(12), 4577\u20134593.","journal-title":"Applied Intelligence"},{"issue":"Jan","key":"9576_CR45","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(Jan), 1\u201330.","journal-title":"Journal of Machine Learning Research"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09576-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-022-09576-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09576-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T13:41:01Z","timestamp":1656596461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-022-09576-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,23]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["9576"],"URL":"https:\/\/doi.org\/10.1007\/s11277-022-09576-3","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,23]]},"assertion":[{"value":"31 January 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have not disclosed any competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}