지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.8
- 수록면
- 1,013 - 1,026 (14page)
- DOI
- 10.9717/kmms.2023.26.8.1013
이용수
초록· 키워드
오류제보하기
Phishing websites based on social engineering are significant cyber threats in the web environment. Recently, a number of studies have been implemented to detect phishing websites using AI (Artificial Intelligence), and they have demonstrated excellent detection performance. However, most of the proposed AI models are black-box. By the nature of black-box, it is difficult to explain how AI models determine if a website is phishing or not. Moreover, false negative is inevitable in the detection system using AI models. Therefore, it is unreliable to detect phishing websites based on the prediction result of an AI model. Because of these limitations, users need to interpret the output of an AI model and make the final decision. In this paper, we propose an interpretable phishing website detection model based on the XAI (eXplainable Artificial Intelligence) techniques so that users can make a reasonable decision with the interpretation of the outputs from the AI model.
목차
ABSTRACT
1. 서론
2. 관련 연구
3. 배경 이론
4. 제안 방법
5. 실험 결과
6. 결론
REFERENCE
참고문헌 (21)
참고문헌 신청
P.R.G. Hernandes, C.P. Floret, K.F.C. De Almeida, V.C. Da Silva, J.P. Papa, K.A.P. Da Costa, et al., “Phishing Detection Using Url-Based Xai Techniques,” IEEE Symposium Series on Computational Intelligence, pp. 1-6, 2021.
B. Wei, R.A. Hamad, L. Yang, X. He, H. Wang, B. Gao, W.L. Woo, et al., “A Deep-Learning-Driven Light-Weight Phishing Detection Sensor,” Sensors, Vol. 19, No. 19, 4258, 2019.
M.C. Calzarossa, P. Giudici, and R. Zieni, “Explainable Machine Learning for Phishing Feature Detection,” Quality and Reliability Engineering International, pp. 1-12, 2023.
N. Aslam, I.U. Khan, S. Mirza, A. AlOwayed, F.M. Anis, R.M. Aljuaid, et al., “Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI),” Sustainability, Vol. 14, No. 12, 7375, 2022.
B.B. Gupta, K. Yadav, I. Razzak, K. Psannis, A. Castiglione, X. Chang, “A Novel Approach for Phishing URLs Detection Using Lexical Based Machine Learning in a Real-Time Environment,” Computer Communications, Vol. 175, No. 1, pp. 47-57, 2021.
B. Wei, R.A. Hamad, L. Yang, X. He, H. Wang, B. Gao, W.L. Woo, et al., “A Deep-Learning-Driven Light-Weight Phishing Detection Sensor,” Sensors, Vol. 19, No. 19, 4258, 2019.
M.C. Calzarossa, P. Giudici, and R. Zieni, “Explainable Machine Learning for Phishing Feature Detection,” Quality and Reliability Engineering International, pp. 1-12, 2023.
N. Aslam, I.U. Khan, S. Mirza, A. AlOwayed, F.M. Anis, R.M. Aljuaid, et al., “Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI),” Sustainability, Vol. 14, No. 12, 7375, 2022.
B.B. Gupta, K. Yadav, I. Razzak, K. Psannis, A. Castiglione, X. Chang, “A Novel Approach for Phishing URLs Detection Using Lexical Based Machine Learning in a Real-Time Environment,” Computer Communications, Vol. 175, No. 1, pp. 47-57, 2021.
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UCI(KEPA) : I410-ECN-0102-2023-004-002035658