{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T22:30:31Z","timestamp":1775946631013,"version":"3.50.1"},"reference-count":26,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>Malicious websites pose significant social risks, necessitating automatic, efficient, and accurate identification methods. This paper proposes a POST traffic classification method based on website templates to identify abnormal traffic from gambling websites. Using Fiddler, POST message data is collected from several gambling sites, extracting features like URLs, cookie parameters, and request body parameters to create a Gambling Website Single POST Message Dataset (GSPD). These features are converted into vector representations withWord2Vec and TF-IDF techniques. Hierarchical clustering identifies template-generated types, achieving unsupervised template recognition. Using clustering results, individual POST messages are labeled and features are extracted using TF-IDF and mutual information methods. The parameters of a Support Vector Machine (SVM) are then optimized with the Particle Swarm Optimization (PSO) algorithm for optimal classification. Experimental results show the model?s excellent performance, with a test set accuracy of 0.9985 and high precision, recall, and F1-scores, effectively identifying gambling and other illegal websites.<\/jats:p>","DOI":"10.2298\/csis240728069f","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T11:26:14Z","timestamp":1730805974000},"page":"79-103","source":"Crossref","is-referenced-by-count":1,"title":["Anomalous traffic identification method for POST messages based on gambling website templates"],"prefix":"10.2298","volume":"22","author":[{"given":"Zhimin","family":"Feng","sequence":"first","affiliation":[{"name":"College of information Engineering, Shanghai Maritime University, Shanghai, China"}]},{"given":"Dezhi","family":"Han","sequence":"additional","affiliation":[{"name":"College of information Engineering, Shanghai Maritime University, Shanghai, China"}]},{"given":"Songyang","family":"Wu","sequence":"additional","affiliation":[{"name":"Network Security Center, The Third Research Institute of the Ministry of Public Security, Shanghai, China"}]},{"given":"Wenqi","family":"Sun","sequence":"additional","affiliation":[{"name":"Network Security Center, The Third Research Institute of the Ministry of Public Security, Shanghai, China"}]},{"given":"Shuxin","family":"Shi","sequence":"additional","affiliation":[{"name":"College of information Engineering, Shanghai Maritime University, Shanghai, China"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Auer, M., Griffiths, M.D.: Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting. Journal of Gambling Studies 39(3), 1273-1294 (2023)","DOI":"10.1007\/s10899-022-10139-1"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Cai, S., Han, D., Li, D.: A feedback semi-supervised learning with meta-gradient for intrusion detection. IEEE Systems Journal 17(1), 1158-1169 (2022)","DOI":"10.1109\/JSYST.2022.3197447"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Cernica, I., Popescu, N.: Computer vision based framework for detecting phishing webpages. In: 2020 19th RoEduNet Conference: Networking in Education and Research (RoEduNet). pp. 1-4. IEEE (2020)","DOI":"10.1109\/RoEduNet51892.2020.9324850"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zheng, R., Zhou, A., Liao, S., Liu, L.: Automatic detection of pornographic and gambling websites based on visual and textual content using a decision mechanism. Sensors 20(14), 3989 (2020)","DOI":"10.3390\/s20143989"},{"key":"ref5","unstructured":"Fan, Y., Yang, T.,Wang, Y., Jiang, G.: Illegal website identification method based on url feature detection. Comput. Eng 44, 171-177 (2018)"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Gu, Z., Gou, G., Liu, C., Yang, C., Zhang, X., Li, Z., Xiong, G.: Let gambling hide nowhere: Detecting illegal mobile gambling apps via heterogeneous graph-based encrypted traffic analysis. Computer Networks 243, 110278 (2024)","DOI":"10.1016\/j.comnet.2024.110278"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Gupta, J., Pathak, S., Kumar, G.: Aquila coyote-tuned deep convolutional neural network for the classification of bare skinned images in websites. International Journal of Machine Learning and Cybernetics 13(10), 3239-3254 (2022)","DOI":"10.1007\/s13042-022-01591-x"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Jain, A.K., Gupta, B.B.: A machine learning based approach for phishing detection using hyperlinks information. Journal of Ambient Intelligence and Humanized Computing 10, 2015-2028 (2019)","DOI":"10.1007\/s12652-018-0798-z"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Kairouz, S., Costes, J.M., Murch, W.S., Doray-Demers, P., Carrier, C., Eroukmanoff, V.: Enabling new strategies to prevent problematic online gambling: A machine learning approach for identifying at-risk online gamblers in france. International Gambling Studies 23(3), 471-490 (2023)","DOI":"10.1080\/14459795.2022.2164042"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Li, D., Han, D., Weng, T.H., Zheng, Z., Li, H., Liu, H., Castiglione, A., Li, K.C.: Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey. Soft Computing 26(9), 4423-4440 (2022)","DOI":"10.1007\/s00500-021-06496-5"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Li, D., Han, D., Zheng, Z.,Weng, T.H., Li, K.C., Li, M., Cai, S.: Does short-and-distort scheme really exist? a bitcoin futures audit scheme through birch & bpnn approach. Computational Economics 63(4), 1649-1671 (2024)","DOI":"10.1007\/s10614-023-10378-3"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Li, J., Han, D., Weng, T.H., Wu, H., Li, K.C., Castiglione, A.: A secure data storage and sharing scheme for port supply chain based on blockchain and dynamic searchable encryption. Computer Standards & Interfaces 91, 103887 (2025)","DOI":"10.1016\/j.csi.2024.103887"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Li, J., Han, D., Wu, Z., Wang, J., Li, K.C., Castiglione, A.: A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control. Future Generation Computer Systems 142, 195-211 (2023)","DOI":"10.1016\/j.future.2022.12.037"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Li, L., Gou, G., Xiong, G., Cao, Z., Li, Z.: Identifying gambling and porn websites with image recognition. In: Advances in Multimedia Information Processing-PCM 2017: 18th Pacific-Rim Conference on Multimedia, Harbin, China, September 28-29, 2017, Revised Selected Papers, Part II 18. pp. 488-497. Springer (2018)","DOI":"10.1007\/978-3-319-77383-4_48"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Liu, D., Lee, J.H., Wang, W., Wang, Y.: Malicious websites detection via cnn based screenshot recognition. In: 2019 International Conference on Intelligent Computing and its Emerging Applications (ICEA). pp. 115-119. IEEE (2019)","DOI":"10.1109\/ICEA.2019.8858300"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Sun, G., Ye, F., Chai, T., Zhang, Z., Tong, X., Prasad, S.: Gambling domain name recognition via certificate and textual analysis. The Computer Journal 66(8), 1829-1839 (2023)","DOI":"10.1093\/comjnl\/bxac043"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Syahputra, H., Wibowo, A.: Comparison of support vector machine (svm) and random forest algorithm for detection of negative content on websites. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 9(1), 165-173 (2023)","DOI":"10.26555\/jiteki.v9i1.25861"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Urvoy, T., Chauveau, E., Filoche, P., Lavergne, T.: Tracking web spam with html style similarities. ACM Transactions on the Web (TWEB) 2(1), 1-28 (2008)","DOI":"10.1145\/1326561.1326564"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Wang, C., Xue, P., Zhang, M., Hu, M.: Identifying gambling websites with co-training. In: SEKE. pp. 598-603 (2022)","DOI":"10.18293\/SEKE2022-106"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, M., Shi, F., Xue, P., Li, Y.: A hybrid multimodal data fusion-based method for identifying gambling websites. Electronics 11(16), 2489 (2022)","DOI":"10.3390\/electronics11162489"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Yang, P., Zhao, G., Zeng, P.: Phishing website detection based on multidimensional features driven by deep learning. IEEE access 7, 15196-15209 (2019)","DOI":"10.1109\/ACCESS.2019.2892066"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Zhang, W., Jiang, Q., Chen, L., Li, C.: Two-stage elm for phishing web pages detection using hybrid features. World Wide Web 20, 797-813 (2017)","DOI":"10.1007\/s11280-016-0418-9"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Zhao, J., Shao, M., Peng, H., Wang, H., Li, B., Liu, X.: Porn2vec: A robust framework for detecting pornographic websites based on contrastive learning. Knowledge-Based Systems 228, 107296 (2021)","DOI":"10.1016\/j.knosys.2021.107296"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Zhou, S., Ruan, L., Xu, Q., Chen, M.: Multimodal fraudulent website identification method based on heterogeneous model ensemble. China Communications 20(5), 263-274 (2023)","DOI":"10.23919\/JCC.fa.2022-0234.202305"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Zhou, S., Xu, C., Xu, R., Ding, W., Chen, C., Xu, X.: Image recognition model of fraudulent websites based on image leader decision and inception-v3 transfer learning. China Communications 21(1), 215-227 (2024)","DOI":"10.23919\/JCC.fa.2023-0450.202401"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Zuhair, H., Selamat, A.: Phishing hybrid feature-based classifier by using recursive features subset selection and machine learning algorithms. In: Recent Trends in Data Science and Soft Computing: Proceedings of the 3rd International Conference of Reliable Information and Communication Technology (IRICT 2018). pp. 267-277. Springer (2019)","DOI":"10.1007\/978-3-319-99007-1_26"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T09:23:35Z","timestamp":1741166615000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142400069F"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.2298\/csis240728069f","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}