Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhibo Zhang, Hussam Al Hamadi, Ernesto Damiani, Chan Yeob Yeun, Fatma, Taher

TL;DR
This survey reviews the current state of Explainable Artificial Intelligence (XAI) methods applied to cyber security, emphasizing the need for transparency in AI models to improve trust and effectiveness in defense mechanisms.
Contribution
It provides the first comprehensive overview of XAI applications specifically in cyber security, highlighting challenges and future research directions.
Findings
XAI enhances trust and interpretability in cyber security models.
Most AI-based cyber security tools lack transparency, limiting user confidence.
The survey identifies gaps and opportunities for applying XAI in cyber defense.
Abstract
This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the black-box manner, meaning that security experts and customers are unable to explain how such procedures reach particular…
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