사용자 의사결정을 고려한 XAI(eXplainable Artificial intelligence) 기반의 피싱 사이트 탐지 모델 = Phishing Website Detection Model for User Decision Making Based on XAI

다국어 초록 (Multilingual Abstract)

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.

참고문헌 (Reference)

1 강홍구 ; 신삼신 ; 김대엽 ; 박순태, "다중 머신러닝 알고리즘을 이용한 악성 URL 예측 시스템 설계 및 구현" 한국멀티미디어학회 23 (23): 1396-1405, 2020

2 M.T. Ribeiro, "Why Should I Trust You?, Explaining the Predictions of any Classifier" Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135-1144, 2016

3 H. Le, "URLNet: Learning a URL Representation With Deep Learning for Malicious URL Detection"

4 F. Tajaddodianfar, "Texception : a Character/Word-Level Deep Learning Model for Phishing URL Detection" 2857-2861, 2020

5 P.R.G. Hernandes, "Phishing Detection Using Url-Based Xai Techniques" 1-6, 2021

6 S. Chhabra, "Phi. sh/$ ocial: the Phishing Landscape Through Short Urls" 92-101, 2011

7 D. Sahoo, "Malicious URL Detection Using Machine Learning: A Survey"

8 N. Aslam, "Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)" 14 (14): 7375-, 2022

9 M.C. Calzarossa, "Explainable Machine Learning for Phishing Feature Detection" 1-12, 2023

10 H. Yan, "Detecting Malicious URLS Using a Deep Learning Approach Based on Stacked Denoising Autoencoder" 372-388, 2019

11 Y. Alshboul, "Detecting Malicious Short URLs on Twitter"

12 S.J. Bu, "Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection" 10 (10): 1492-, 2021

13 Y. Kim, "Convolutional Neural Networks for Sentence Classification"

14 A. C. Bahnsen, "Classifying Phishing URLs Using Recurrent Neural Networks" 1-8, 2017

15 J. Hong, "Adaptive Autonomous Secure Cyber Systems" Adaptive Autonomous Secure Cyber Systems 253-267, 2020

16 "APWG(Anti-Phishing Working Group)"

17 S.M. Lundberg, "A Unified Approach to Interpreting Model Predictions"

18 B.B. Gupta, "A Novel Approach for Phishing URLs Detection Using Lexical Based Machine Learning in a Real-Time Environment" 175 (175): 47-57, 2021

19 M. Darling, "A Lexical Approach for Classifying Malicious URLs" 195-202, 2015

20 S. Garera, "A Framework for Detection and Measurement of Phishing Attacks" 1-8, 2007

21 B. Wei, "A Deep-Learning-Driven Light-Weight Phishing Detection Sensor" 19 (19): 4258-, 2019