Position: The Explainability Paradox - Challenges for XAI in Malware Detection and Analysis

IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 554–561

Abstract

Malware poses a significant threat to global cy-bersecurity, with machine learning emerging as the primary method for its detection and analysis. However, the opaque nature of machine learning's decision-making process of-ten leads to confusion among stakeholders, undermining their confidence in the detection outcomes. To enhance the trustworthiness of malware detection, Explainable Artificial Intelligence (XAI) is employed to offer transparent and comprehensible explanations of the detection mechanisms, which enable stakeholders to gain a deeper understanding of detection mechanisms and assist in developing defensive strategies. Despite the recent XAI advancements, several challenges remain unaddressed. In this paper, we explore the specific obstacles encountered in applying XAI to malware detection and analysis, aiming to provide a road map for future research in this critical domain.

Authors 1

1. Olga Gadyatskaya Aachen

   Leiden University

   Affiliation as printed

   Leiden University,LIACS,Leiden,The Netherlands