Open Access
Issue
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
Article Number 01008
Number of page(s) 13
Section Software Engineering & Information Technology
DOI https://doi.org/10.1051/itmconf/20235701008
Published online 10 November 2023
  1. Chiew Kang Leng Kelvin Sheng Chek Yong and Choon Lin Tan 2018 A survey of phishing attacks: Their types, vectors and technical approaches. Expert Systems with Applications. 106: 1-20. Doi: https://doi.org/10.1016/j.eswa.2018.03.050 [CrossRef] [Google Scholar]
  2. Rao Routhu Srinivasa and Alwyn Roshan Pais 2019 Detection of phishing websites using an efficient feature based machine learning framework. Neural Computing and Applications. 31(8): 3851-3873. Doi: https://doi.org/10.1007/s00521-017-3305-0 [CrossRef] [Google Scholar]
  3. http://www2.deloitte.com/content/dam/Deloitte/sg/Documents/risk/searisk-cyber-101-part10.pdf [Google Scholar]
  4. G J W Kathrine P M Praise A A Rose and E C Kalaivani 2019 Variants of phishing attacks and their detection techniques 3rd International Conference on Trends in Electronics and Informatics (ICOEI). 255-259. DOI: 10.1109/ICOEI.2019.8862697 [Google Scholar]
  5. Rao R S Pais A R 2019 Detection of phishing websites using an efficient feature-based machine learning framework. Neural Comput & Applic. 31: 3851–3873. DOI: https://doi.org/10.1007/s00521-017-3305-0 [CrossRef] [Google Scholar]
  6. Rao R S Pais A R and Anand P 2020 A heuristic technique to detect phishing websites using TWSVM classifier. Neural Comput & Applic DOI: https://doi.org/10.1007/s00521-020-05354-z [Google Scholar]
  7. S Roopak A P Vijayaraghavan and T Thomas 2019 On Effectiveness of Source Code and SSL Based Features for Phishing Website Detection. 1st International Conference on Advanced Technologies in Intelligent Control. Environment, Computing & Communication Engineering (ICATIECE):172-175. DOI: 10.1109/ICATIECE45860.2019.9063824 [Google Scholar]
  8. A Nakamura and F Dobashit 2019 Proactive Phishing Sites Detection. IEEE/WIC/ACM International Conference on Web Intelligence (WI). 443-448 DOI: https://doi.org/10.1145/3350546.3352565 [CrossRef] [Google Scholar]
  9. F Tajaddodianfar J W Stokes and A Gururajan 2020 Texception: A Character/WordLevel Deep Learning Model for Phishing URL Detection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2857-2861 DOI: 10.1109/ICASSP40776.2020.9053670 [Google Scholar]
  10. K Althobaiti G Rummani and K Vaniea 2019 A Review of Human and Computer Facing URL Phishing Features. IEEE European Symposium on Security and Privacy Workshops. 182-191 DOI: 10.1109/EuroSPW.2019.00027 [Google Scholar]
  11. Carlo Marcelo Revoredo da Silva Eduardo Luzeiro Feitosa Vinicius Cardoso Garcia 2020 Heuristic based strategy for Phishing prediction: A survey of URL-based approach. Computers & Security, 101613 DOI: https://doi.org/10.1016/j.cose.2019.101613 [Google Scholar]
  12. Athulya A A and K Praveen 2020 Towards the detection of phishing attacks. 4th international conference on trends in electronics and informatics (ICOEI)(48184). DOI: 10.1109/ICOEI48184.2020.9142967 [Google Scholar]
  13. Sahar Abdelnabi Katharina Krombholz and Mario Fritz 2020 VisualPhishNet: ZeroDay Phishing Website Detection by Visual Similarity. Association for Computing Machinery. 1681–1698 DOI: https://doi.org/10.1145/3372297.3417233 [Google Scholar]
  14. S Haruta H Asahina and I Sasase 2017 Visual Similarity-Based Phishing Detection Scheme Using Image and CSS with Target Website Finder. IEEE Global Communications Conference. pp. 1-6. DOI: 10.1109/GLOCOM.2017.8254506 [Google Scholar]
  15. M M Yadollahi F Shoeleh E Serkani A Madani and H Gharaee 2019 An Adaptive Machine Learning Based Approach for Phishing Detection Using Hybrid Features. Web Research. pp. 281-286 DOI: 10.1109/ICWR.2019.8765265 [Google Scholar]
  16. Jain AK Gupta B B 2019 A machine learning based approach for phishing detection using hyperlinks information. J Ambient Intell Human Comput 10. 2015–2028 DOI: https://doi.org/10.1007/s12652-018-0798-z [CrossRef] [Google Scholar]
  17. J Kumar A Santhanavijayan B Janet B Rajendran and B S Bindhumadhava 2020 Phishing Website Classification and Detection Using Machine Learning. Computer Communication and Informatics. pp. 1-6 DOI: https://doi.org/10.48550/arXiv.2103.12739 [Google Scholar]
  18. https://www.kdnuggets.com/2020/02/deepneural-networks.html [Google Scholar]
  19. I Saha D Sarma R J Chakma M N Alam A Sultana and S Hossain 2020 Phishing Attacks Detection using Deep Learning Approach. Smart Systems and Inventive Technology. pp. 1180-1185 DOI: 10.1109/ICSSIT48917.2020.9214132 [Google Scholar]
  20. Jain Ankit Kumar and B B Gupta 2022 A survey of phishing attack techniques defence mechanisms and open research challenges. Enterprise Information Systems. 16(4): 527-565 DOI: https://doi.org/10.1080/17517575.2021.1896786 [CrossRef] [Google Scholar]
  21. Jalil Sajjad Muhammad Usman and Alvis Fong 2022 Highly accurate phishing URL detection based on machine learning. Journal of Ambient Intelligence and Humanized Computing: 1-19 DOI: https://doi.org/10.1007/s12652-022-04426-3 [Google Scholar]
  22. Ramana A V K Lakshmana Rao and Routhu Srinivasa Rao 2021 Stop-Phish: an intelligent phishing detection method using feature selection ensemble. Social Network Analysis and Mining. 11(1): 1-9 DOI: https://doi.org/10.1007/s13278-021-00829-w [CrossRef] [Google Scholar]
  23. Harinahalli Lokesh Gururaj and Goutham BoreGowda 2021 Phishing website detection based on effective machine learning approach. Journal of Cyber Security Technology. 5(1): 1-14 DOI: https://doi.org/10.1080/23742917.2020.1813396 [CrossRef] [Google Scholar]
  24. Tang Lizhen and Qusay H Mahmoud 2021 A survey of machine learning-based solutions for phishing website detection. Machine Learning and Knowledge Extraction. 3(3): 672-694 DOI: https://hdl.handle.net/10155/1446 [CrossRef] [Google Scholar]
  25. https://www.alexa.com/topsites [Google Scholar]
  26. http://index.commoncrawl.org/ [Google Scholar]
  27. https://www.phishtank.com/developer_info.php [Google Scholar]
  28. https://openphish.com/ [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.