ARCHIVES

1. Srushti Patil, and Sudhir Dhage, “A Methodical Overview On Phishing Detection Along With An Organized Way To Construct an
Anti Phishing Framework”, 2019 5th International Conference On Advanced Computing & Communication System(ICACCS), pp.
1-6.
2. Huaping Yuan, Xu Chen, Yukun Li, Zhenguo Yang and Wenyin Liu, “Detecting Phishing Websites and Targets Based On URLs
and Webpage Links”, 2018 24th International Conference on Pattern Recognition(ICPR) Beijing, China, August 20 -24, 2021.
3. Vaibhav Patil, Pritesh Thakkar, Cjirag Shah, Tushar Bhat, Prof. S. P.Godse, “Detection and Prevention of Phishing Website s using
Machine Learning Approach”, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68 –73.
4. Mustafa AYDIN and Nazifa BAYKAL, “Feature Extraction and Classification Phishing Websites Based on URL”, 2021.
5. C. Emilin Shyni, Anesh D Sundar and G. S. Edwin Ebby. “Phishing Detection In Websites Using Parse Tree Validation”, 2018
Recent Advances On Engineering , Technology and Computational Sciences(RAETCS).
6. Shraddha Parekh, Dhwanil Parikh, Srushti Kotak and Prof. Smita Sankhi, “A New Method For Detection of Phishing W ebsites:
URL Detection”, Proceedings of the 2nd International Conference on Inventice Communicastion and Computational
Technologies(ICICCT 2018) IEEE Explorer Complaint-Part Number: CFP18BAC-ART:ISBN: 978-1 5386-1974-2
7. Anu Vazhayil, Vinaya Kumar R and Soman KP, “Comparative Srudy Of The Detcetion Of Malicious URLs Using Shallow and
Deep Netoworks “, 9th ICCCNT2018 July 10-12,2018,IISC,Bangluru,India.
8. A. Mohammad, M. Thabtah, and F. McCluskey, “Predicting phishing websites based on self-structuring neural network,” Neural
Computing and Applications, vol. 25, no. 2, pp. 443–458, 2014.
9. R. Verma and K. Dyer, “On the character of phishing URLs: Accurate and robust statistical learning classifiers,” 2015 IEEE Se curity
and Privacy Workshops (SPW), pp. 82–87, 2015.
10. I. R. Pandey, A. Joshi, and H. Kumar, “Malicious URL detection using machine learning,” 2018 International Conference on
Computing, Power and Communication Technologies (GUCON), pp. 285–290, 2018.
11. Y. Zhang, J. Hong, and L. Cranor, “CANTINA: A content-based approach to detecting phishing web sites,” Proceedings of the 16th
International Conference on World Wide Web (WWW), pp. 639–648, 2007.
12. S. Marchal, J. François, R. State, and T. Engel, “PhishStorm: Detecting phishing with streaming analytics,” IEEE Transa ctions on
Network and Service Management, vol. 11, no. 4, pp. 458–471, 2014.
13. N. Abdelhamid, A. Ayesh, and F. Thabtah, “Phishing detection based associative classification data mining,” Expert Systems wi th
Applications, vol. 41, no. 13, pp. 5948–5959, 2014.
14. S. Jain and V. Gupta, “Towards detection of phishing webpages: An analytical review,” 2020 10th International Conference on
Cloud Computing, Data Science & Engineering (Confluence), pp. 588–593, 2020.
15. L. Xiang, C. Wu, Q. Liu, and J. Wang, “A hierarchical phishing detection method based on URL features,” 2018 IEEE 3rd Advanced
Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1996 –2000, 2018.
16. A. Jain and B. B. Gupta, “Phishing detection: Analysis of visual similarity based approaches,” Security and Communication
Networks, vol. 2017, pp. 1–20, 2017.
17. S. Garera, N. Provos, M. Chew, and A. Rubin, “A framework for detection and measurement of phishing attacks,” Proceedings of
the 2007 ACM Workshop on Recurring Malcode (WORM), pp. 1–8, 2007.