{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T14:28:49Z","timestamp":1749479329940,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030977764"},{"type":"electronic","value":"9783030977771"}],"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\/978-3-030-97777-1_41","type":"book-chapter","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T17:03:05Z","timestamp":1647363785000},"page":"493-505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Using a Machine Learning Model for Malicious URL Type Detection"],"prefix":"10.1007","author":[{"given":"Suet Ping","family":"Tung","sequence":"first","affiliation":[]},{"given":"Ka Yan","family":"Wong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6917-4234","authenticated-orcid":false,"given":"Ievgeniia","family":"Kuzminykh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4750-7864","authenticated-orcid":false,"given":"Taimur","family":"Bakhshi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1788-547X","authenticated-orcid":false,"given":"Bogdan","family":"Ghita","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"41_CR1","unstructured":"We Are Social, Hoootsuite: Digital 2021 Global Overview Report. Datareportal.com. 299 (2021)"},{"key":"41_CR2","unstructured":"Google: Google: Transparency Report. Google Transpar. Rep. (2010)"},{"key":"41_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2010.5462216","author":"P Prakash","year":"2010","unstructured":"Prakash, P., Kumar, M., Rao Kompella, R., Gupta, M.: PhishNet: predictive blacklisting to detect phishing attacks. Proc. IEEE INFOCOM. (2010). https:\/\/doi.org\/10.1109\/INFCOM.2010.5462216","journal-title":"Proc. IEEE INFOCOM."},{"key":"41_CR4","unstructured":"Felegyhazi, M., Kreibich, C., Paxson, V.: On the potential of proactive domain blacklisting. LEET 2010 - 3rd USENIX Work. Large-Scale Exploit. Emergent Threat. Botnets, Spyware, Worms, More. (2010)"},{"key":"41_CR5","doi-asserted-by":"publisher","unstructured":"Sinha, S., Bailey, M., Jahanian, F.: Shades of Grey: on the effectiveness of reputation-based blacklists. In: 3rd International Conference Malicious Unwanted Software, MALWARE 2008. 57\u201364 (2008). https:\/\/doi.org\/10.1109\/MALWARE.2008.4690858","DOI":"10.1109\/MALWARE.2008.4690858"},{"key":"41_CR6","unstructured":"Sahoo, D., Liu, C., Hoi, S.C.H.: Malicious URL detection using machine learning: a survey. (2017)"},{"key":"41_CR7","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.: Phishing detection based Associative Classification data mining. Expert Syst. Appl. 41, 5948\u20135959 (2014). https:\/\/doi.org\/10.1016\/j.eswa.2014.03.019","journal-title":"Expert Syst. Appl."},{"key":"41_CR8","doi-asserted-by":"publisher","unstructured":"Jeeva, S.C., Rajsingh, E.B.: Intelligent phishing url detection using association rule mining. Human-centric Comp. Inf. Sci. 6, (2016). https:\/\/doi.org\/10.1186\/s13673-016-0064-3","DOI":"10.1186\/s13673-016-0064-3"},{"key":"41_CR9","doi-asserted-by":"publisher","DOI":"10.1145\/3366030.3366064","author":"ES Aung","year":"2019","unstructured":"Aung, E.S., Yamana, H.: URL-based phishing detection using the entropy of non- A lphanumeric characters. ACM Int. Conf. Proceeding Ser. (2019). https:\/\/doi.org\/10.1145\/3366030.3366064","journal-title":"ACM Int. Conf. Proceeding Ser."},{"key":"41_CR10","first-page":"335","volume":"8","author":"R Ravi","year":"2021","unstructured":"Ravi, R., Shillare, A.A., Bhoir, P.P., Charumathi, K.S.: URL based email phishing detection application. Int. Res. J. Eng. Technol. 8, 335\u2013360 (2021)","journal-title":"Int. Res. J. Eng. Technol."},{"key":"41_CR11","unstructured":"Verizon: Data Breach Investigations Report (DBIR). Comput. Fraud Secur. 12, 8 (2019)"},{"key":"41_CR12","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.: A new fast associative classification algorithm for detecting phishing websites. Appl. Soft Comput. J. 48, 729\u2013734 (2016). https:\/\/doi.org\/10.1016\/j.asoc.2016.08.005","journal-title":"Appl. Soft Comput. J."},{"key":"41_CR13","unstructured":"Aung, E.S., Zan, T., Yamana, H.: A survey of URL-based phishing detection. pp. 1\u20138 (2019)"},{"key":"41_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/e23020182","volume":"23","author":"S Kumi","year":"2021","unstructured":"Kumi, S., Lim, C., Lee, S.G.: Malicious url detection based on associative classification. Entropy 23, 1\u201312 (2021). https:\/\/doi.org\/10.3390\/e23020182","journal-title":"Entropy"},{"key":"41_CR15","doi-asserted-by":"publisher","unstructured":"Shantanu, D., Janet, B., Kumar, R.J.A.: Malicious URL detection: a comparative study. In: Proceedings of International Conference Artificial Intelligence Smart System ICAIS 2021, pp. 1147\u20131151 (2021). https:\/\/doi.org\/10.1109\/ICAIS50930.2021.9396014","DOI":"10.1109\/ICAIS50930.2021.9396014"},{"key":"41_CR16","doi-asserted-by":"publisher","unstructured":"Tan, G., Zhang, P., Liu, Q., Liu, X., Zhu, C., Dou, F.: Adaptive malicious url detection: learning in the presence of concept drifts. In: Proceedings of 17th IEEE International Conference (TrustCom\/BigDataSE), pp. 737\u2013743 (2018). https:\/\/doi.org\/10.1109\/TrustCom\/BigDataSE.2018.00107","DOI":"10.1109\/TrustCom\/BigDataSE.2018.00107"},{"key":"41_CR17","doi-asserted-by":"publisher","unstructured":"Srinivasan, S., Vinayakumar, R., Arunachalam, A., Alazab, M., Soman, K.: DURLD: malicious URL detection using deep learning-based character level representations. In: Stamp, M., Alazab, M., Shalaginov, A. (eds.) Malware Analysis Using Artificial Intelligence and Deep Learning. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-62582-5_21","DOI":"10.1007\/978-3-030-62582-5_21"},{"key":"41_CR18","doi-asserted-by":"publisher","unstructured":"Dhingra, B., Zhou, Z., Fitzpatrick, D., Muehl, M., Cohen, W.W.: Tweet2Vec: character-based distributed representations for social media. In: 54th Annual Meeting Association Computer Linguistics ACL 2016, pp. 269\u2013274 (2016). https:\/\/doi.org\/10.18653\/v1\/p16-2044","DOI":"10.18653\/v1\/p16-2044"},{"key":"41_CR19","doi-asserted-by":"publisher","unstructured":"Anderson, H.S., Woodbridge, J., Filar, B.: DeepDGA: adversarially-tuned domain generation and detection. In: AISec 2016 \u2013 Proceedings of 2016 ACM Work. Artificial Intelligence Security co-located with CCS 2016, pp. 13\u201321 (2016). https:\/\/doi.org\/10.1145\/2996758.2996767","DOI":"10.1145\/2996758.2996767"},{"key":"41_CR20","doi-asserted-by":"publisher","unstructured":"Kuzminykh, I., Shevchuk, D., Shiaeles, S., Ghita, B.: Audio interval retrieval using convolutional neural networks. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds.) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. LNCS, vol. 12525. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65726-0_21","DOI":"10.1007\/978-3-030-65726-0_21"},{"key":"41_CR21","doi-asserted-by":"publisher","unstructured":"Johnson, C., Khadka, B., Basnet, R.B., Doleck, T.: Towards detecting and classifying malicious urls using deep learning. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 11, 31\u201348 (2020). https:\/\/doi.org\/10.22667\/JOWUA.2020.12.31.031","DOI":"10.22667\/JOWUA.2020.12.31.031"},{"key":"41_CR22","doi-asserted-by":"publisher","unstructured":"Li, T., Kou, G., Peng, Y.: Improving malicious URLs detection via feature engineering: linear and nonlinear space transformation methods. Inf. Syst. 91, (2020). https:\/\/doi.org\/10.1016\/j.is.2020.101494","DOI":"10.1016\/j.is.2020.101494"},{"key":"41_CR23","doi-asserted-by":"publisher","DOI":"10.1145\/3433174.3433592","author":"V Vundavalli","year":"2020","unstructured":"Vundavalli, V., Barsha, F., Masum, M., Shahriar, H., Haddad, H.: Malicious URL detection using supervised machine learning techniques. ACM Int. Conf. Proceeding Ser. (2020). https:\/\/doi.org\/10.1145\/3433174.3433592","journal-title":"ACM Int. Conf. Proceeding Ser."},{"key":"41_CR24","unstructured":"Urcuqui, C.: Malicious and Benign Websites dataset. https:\/\/www.kaggle.com\/xwolf12\/malicious-and-benign-websites. Accessed 12 Jul 2021"},{"key":"41_CR25","unstructured":"Choi, H., Zhu, B.B., Lee, H.: Detecting malicious web links and identifying their attack types. WebApps. 11 (2011)"},{"key":"41_CR26","doi-asserted-by":"publisher","unstructured":"Ma\u0161etic, Z., Subasi, A., Azemovic, J.: Malicious web sites detection using C4.5 decision tree. Southeast Eur. J. Soft Comput. 5 (2016). https:\/\/doi.org\/10.21533\/scjournal.v5i1.109","DOI":"10.21533\/scjournal.v5i1.109"},{"key":"41_CR27","doi-asserted-by":"publisher","unstructured":"Eshete, B., Villafiorita, A., Weldemariam, K., Zulkernine, M.: EINSPECT: evolution-guided analysis and detection of malicious web pages. In: Proceedings of International Computing Software Applied Conference, pp. 375\u2013380 (2013). https:\/\/doi.org\/10.1109\/COMPSAC.2013.63","DOI":"10.1109\/COMPSAC.2013.63"},{"key":"41_CR28","doi-asserted-by":"publisher","unstructured":"Chu, W., Zhu, B.B., Xue, F., Guan, X., Cai, Z.: Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs. IEEE Int. Conf. Commun. 1990\u20131994 (2013). https:\/\/doi.org\/10.1109\/ICC.2013.6654816","DOI":"10.1109\/ICC.2013.6654816"},{"key":"41_CR29","doi-asserted-by":"publisher","unstructured":"Canali, D., Cova, M., Vigna, G., Kruegel, C.: Prophiler: A fast filter for the large-scale detection of malicious web pages. In: Proceedings of 20th International Conference World Wide Web, WWW 2011. pp. 197\u2013206 (2011). https:\/\/doi.org\/10.1145\/1963405.1963436","DOI":"10.1145\/1963405.1963436"},{"key":"41_CR30","unstructured":"Murthy, S. K.: Automatic construction of decision trees from data: a multidisciplinary survey. Data Min. Knowl. Discov. 2(4), 345-89 (1998)"},{"key":"41_CR31","unstructured":"Canadian Institute for Cybersecurity: URL dataset (ISCX-URL-2016)"},{"key":"41_CR32","unstructured":"Amazon: Alexa Internet, www.alexa.com"},{"key":"41_CR33","unstructured":"Castillio, C.: Web Spam Collections. http:\/\/chato.cl\/webspam\/datasets\/uk2007\/. Accessed 12 Jul 2021"},{"key":"41_CR34","unstructured":"OpenPhish: Phishing Intelligence. (2020)"},{"key":"41_CR35","unstructured":"Risk Analytics: DNS-BH - Malware Domain Blocklist. (2021)"},{"key":"41_CR36","doi-asserted-by":"publisher","unstructured":"Breiman, L.: Random Forests. Mach. Learn. 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"41_CR37","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","volume":"36","author":"M Stone","year":"1974","unstructured":"Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B. 36, 111\u2013147 (1974)","journal-title":"J. R. Stat. Soc. Ser. B."}],"container-title":["Lecture Notes in Computer Science","Internet of Things, Smart Spaces, and Next Generation Networks and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-97777-1_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T09:31:18Z","timestamp":1726824678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-97777-1_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030977764","9783030977771"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-97777-1_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NEW2AN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Next Generation Wired\/Wireless Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"St. Petersburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"new2an2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/new2an.info\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"118","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":"35","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":"30% - 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":"2","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":"1.27","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)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}