| Title | Objection!: Identifying Misclassified Malicious Activities with XAI |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Fujita, Koji, Shibahara, Toshiki, Chiba, Daiki, Akiyama, Mitsuaki, Uchida, Masato |
| Conference Name | ICC 2022 - IEEE International Conference on Communications |
| Date Published | may |
| Keywords | Conferences, Cyberspace, feature extraction, machine learning, malicious website detection, Malware, malware detection, Object recognition, pubcrawl, resilience, Resiliency, Scalability, security, xai |
| Abstract | Many studies have been conducted to detect various malicious activities in cyberspace using classifiers built by machine learning. However, it is natural for any classifier to make mistakes, and hence, human verification is necessary. One method to address this issue is eXplainable AI (XAI), which provides a reason for the classification result. However, when the number of classification results to be verified is large, it is not realistic to check the output of the XAI for all cases. In addition, it is sometimes difficult to interpret the output of XAI. In this study, we propose a machine learning model called classification verifier that verifies the classification results by using the output of XAI as a feature and raises objections when there is doubt about the reliability of the classification results. The results of experiments on malicious website detection and malware detection show that the proposed classification verifier can efficiently identify misclassified malicious activities. |
| DOI | 10.1109/ICC45855.2022.9838748 |
| Citation Key | fujita_objection_2022 |
Objection!: Identifying Misclassified Malicious Activities with XAI