The present, past, and future aspects of XAI in cybersecurity
Muqadsa Jabeen, Muhammad Ibrar, Muhammad Saeed · 2025
In the current era of cybersecurity, deep learning (DL), machine learning (ML), and artificial intelligence (AI) algorithms are widely used in various applications, including Android malware detection and web security. Moreover, ML algorithms continue to play a key role in improving cybersecurity solutions. However, they face significant challenges in some DL areas, such as computer vision and natural language processing, particularly in their inability to predict outcomes and make decisions accurately. These challenges underscore the importance of explainable artificial intelligence (XAI). XAI algorithms aim to support the interpretation of human-generated patterns, enabling humans to understand the reasoning behind automated results. Therefore, XAI algorithms are crucial in cybersecurity, as they can assist security professionals overwhelmed by numerous security alerts - many of which are false positives - in identifying potential threats and reducing alert fatigue. This chapter focuses on XAI's current, past, and future roles in cybersecurity. It is a unique and vital area for protecting systems, networks, and software from various attacks. The chapter begins with an overview of cybersecurity architectures and threat types, followed by a discussion of traditional AI techniques and their limitations, which provide a foundation for coherent XAI approaches. It also explores the applications of XAI across several research domains and industries and concludes with key findings to guide future research on XAI in cybersecurity. This study highlights the importance of XAI in mitigating emerging cyber threats.