Phishing URL Detection-Reference-Cited by-同舟云学术

Phishing URL Detection

Published:2022-11-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
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Author:

Kim Taeri¹,Park Noseong²,Hong Jiwon¹,Kim Sang-Wook¹

Affiliation:

1. Hanyang University, Seoul, Republic of Korea

2. Yonsei University, Seoul, Republic of Korea

Funder

The work of Sang-Wook Kim was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)

The work of Sang-Wook Kim was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)

The work of Noseong Park was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3548606.3560615

Reference52 articles.

1. PhishZoo: Detecting Phishing Websites by Looking at Them

2. Phishing URL Detection with Oversampling based on Text Generative Adversarial Networks

3. Classifying phishing URLs using recurrent neural networks

4. Christopher M Bishop and Nasser M Nasrabadi . 2006. Pattern recognition and machine learning . Vol. 4 . Christopher M Bishop and Nasser M Nasrabadi. 2006. Pattern recognition and machine learning. Vol. 4.

5. Fast unfolding of communities in large networks

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