{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T14:04:52Z","timestamp":1784556292830,"version":"3.55.0"},"reference-count":20,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T00:00:00Z","timestamp":1602633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This work proposes an intelligent visual technique for detecting phishing websites. The phishing websites are classified into three categories: very similar, local similar, and non-imitating. For cases of \u2018very similar\u2019, this study uses the wavelet Hashing (wHash) mechanism with a color histogram to evaluate the similarity. In cases of \u2018local similarity\u2019, this study uses the Scale-Invariant Feature Transform (SIFT) technique to evaluate the similarity. This work concerns \u2018very similar\u2019 and \u2018local similar\u2019 cases to detect phishing websites. The results of the experiments reveal that the wHash mechanism with a color histogram is more accurate than the currently used perceptual Hashing (pHash) mechanism. The accuracies of SIFT technique are 97.93%, 98.61%, and 99.95% related to Microsoft, Dropbox, and Bank of America data, respectively.<\/jats:p>","DOI":"10.3390\/sym12101681","type":"journal-article","created":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T07:23:22Z","timestamp":1602919402000},"page":"1681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Intelligent Visual Similarity-Based Phishing Websites Detection"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0400-5514","authenticated-orcid":false,"given":"Jiann-Liang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Wei","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan-Lung","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kim, M., Song, C., Kim, H., Park, D., Kwon, Y., Namkung, E., Harris, I.G., and Carlsson, M. (2019, January 4\u20136). Scam detection assistant: Automated protection from scammers. Proceedings of the 1st International Conference on Societal Automation, Krakow, Poland.","DOI":"10.1109\/SA47457.2019.8938036"},{"key":"ref_2","unstructured":"(2018, February 27). APWG\u2019s Q3 2017 Phishing Activity Trends Report. Available online: http:\/\/docs.apwg.org\/reports\/apwg_trends_report_q3_2017.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1007\/s12652-017-0616-z","article-title":"Two-level authentication approach to protect from phishing attacks in real time","volume":"9","author":"Jain","year":"2018","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.dss.2016.05.005","article-title":"PhishWHO: Phishing webpage detection via identity keywords extraction and target domain name finder","volume":"88","author":"Tan","year":"2017","journal-title":"Decis. Support Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bahnsen, A.C., Bohorquez, E.C., Villegas, S., Vargas, J., and Gonz\u00e1lez, F.A. (2017, January 25\u201327). Classifying phishing URLs using recurrent neural networks. Proceedings of the APWG Symposium on Electronic Crime Research, Scottsdale, AZ, USA.","DOI":"10.1109\/ECRIME.2017.7945048"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Buber, E., D\u0131r\u0131, B., and Sahingoz, O.K. (2017, January 5\u20137). Detecting phishing attacks from URL by using NLP techniques. Proceedings of the International Conference on Computer Science and Engineering, London, UK.","DOI":"10.1109\/UBMK.2017.8093406"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hu, Z., Chiong, R., Pranata, I., Susilo, W., and Bao, Y. (2016, January 24\u201329). Identifying malicious web domains using machine learning techniques with online credibility and performance data. Proceedings of the Congress on Evolutionary Computation, Vancouver, BC, Canada.","DOI":"10.1109\/CEC.2016.7748347"},{"key":"ref_8","first-page":"28","article-title":"New hybrid features for phish website prediction","volume":"8","author":"Zuhair","year":"2016","journal-title":"Int. J. Adv. Soft Comput. Its Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Althobaiti, K., Rummani, G., and Vaniea, K. (2019, January 17\u201319). A Review of human- and computer-facing URL phishing features. Proceedings of the IEEE European Symposium on Security and Privacy Workshop, Stockholm, Sweden.","DOI":"10.1109\/EuroSPW.2019.00027"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17020","DOI":"10.1109\/ACCESS.2017.2743528","article-title":"Phishing-alarm: Robust and efficient phishing detection via page component similarity","volume":"5","author":"Mao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_11","first-page":"1","article-title":"A Method for the Automated Detection of Phishing Websites Through Both Site Characteristics and Image Analysis","volume":"Volume 8408","author":"White","year":"2012","journal-title":"Proceedings of the SPIE"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rao, R.S., and Ali, S.T. (2015, January 4\u20136). A computer vision technique to detect phishing attacks. Proceedings of the 5th International Conference on Communication Systems and Network Technologies, Gwalior, India.","DOI":"10.1109\/CSNT.2015.68"},{"key":"ref_13","unstructured":"Asudeh, O. (2016). A New Real-time Approach for Website Phishing Detection Based on Visual Similarity. [Ph.D. Thesis, The University of Texas at Arlington]."},{"key":"ref_14","unstructured":"Wang, G., Liu, H., Becerra, S., Wang, K., Belongie, S., Shacham, H., and Savage, S. (2011). Verilogo: Proactive Phishing Detection via Logo Recognition, University of California."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Singh, S.P., and Bhatnagar, G. (2017, January 4\u20136). A Robust Image Hashing Based on Discrete Wavelet Transform. Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Singapore.","DOI":"10.1109\/ICSIPA.2017.8120651"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nevriyanto, A., Sutarno, S., Siswanti, S.D., and Erwin, E. (2018, January 5\u20137). Image steganography using combine of discrete wavelet transform and singular value decomposition for more robustness and higher peak signal noise ratio. Proceedings of the International Conference on Electrical Engineering and Computer Science, Mexico City, Mexico.","DOI":"10.1109\/ICECOS.2018.8605205"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, S., and Qin, H. (2009, January 14\u201316). A study of order-based block color feature image retrieval compared with cumulative color histogram method. Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China.","DOI":"10.1109\/FSKD.2009.294"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jin, X., and Kim, J. (2016, January 20\u201322). ArtWork recognition in 360-degree image using 32-hedron based rectilinear projection and scale invariant feature transform. Proceedings of the IEEE International Conference on Electronic Information and Communication Technology, Harbin, China.","DOI":"10.1109\/ICEICT.2016.7879716"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shin, J., Kim, D., and Ruland, C. (2014, January 27\u201330). Content based image authentication using HOG feature descriptor. Proceedings of the IEEE International Conference on Image Processing, Paris, France.","DOI":"10.1109\/ICIP.2014.7026071"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pudchuen, N., and Deelertpaiboon, C. (2018, January 7\u20139). Visual odometry based on k-nearest neighbor matching and robust motion estimation. Proceedings of the International Electrical Engineering Congress, Krabi, Thailand.","DOI":"10.1109\/IEECON.2018.8712323"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/10\/1681\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:21:10Z","timestamp":1760178070000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/10\/1681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,14]]},"references-count":20,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["sym12101681"],"URL":"https:\/\/doi.org\/10.3390\/sym12101681","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,14]]}}}