| Title | Vulnerability Assessment for Deep Learning Based Phishing Detection System |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ogawa, Yuji, Kimura, Tomotaka, Cheng, Jun |
| Conference Name | 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) |
| Keywords | Conferences, Deep Learning, Electronic countermeasures, Human Behavior, phishing, pubcrawl, security |
| Abstract | Recently, the threats of phishing attacks have in-creased. As a countermeasure against phishing attacks, phishing detection systems using deep learning techniques have been considered. However, deep learning techniques are vulnerable to adversarial examples (AEs) that intentionally cause misclassification. When AEs are applied to a deep-learning-based phishing detection system, they pose a significant security risk. Therefore, in this paper, we assess the vulnerability of a phishing detection system by inputting AEs generated based on a dataset that consists of phishing sites' URLs. Moreover, we consider countermeasures against AEs and clarify whether these defense methods can prevent misclassification. |
| DOI | 10.1109/ICCE-TW52618.2021.9602964 |
| Citation Key | ogawa_vulnerability_2021 |
Vulnerability Assessment for Deep Learning Based Phishing Detection System