Objection! Identifying Misclassified Malicious Activities with XAI

Koji Fujita*, Toshiki Shibahara, Daiki Chiba, Mitsuaki Akiyama, Masato Uchida*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2065-2070
Number of pages6
JournalIEEE International Conference on Communications
Volume2022-January
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: 2022 May 162022 May 20

Keywords

  • XAI
  • machine learning
  • malicious website detection
  • malware detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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