  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 conclusions. The
deficiencies of transparency and interpretability of existing Artificial
Intelligence techniques would decrease human users' confidence in the models
utilized for the defense against cyber attacks, especially in current
situations where cyber attacks become increasingly diverse and complicated.
Therefore, it is essential to apply XAI in the establishment of cyber security
models to create more explainable models while maintaining high accuracy and
allowing human users to comprehend, trust, and manage the next generation of
cyber defense mechanisms. Although there are papers reviewing Artificial
Intelligence applications in cyber security areas and the vast literature on
applying XAI in many fields including healthcare, financial services, and
criminal justice, the surprising fact is that there are currently no survey
research articles that concentrate on XAI applications in cyber security.
