Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

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 Internetconnected 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 transparencies 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. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

Explainable machine learning for phishing feature detection

“…Although machine learning approaches have been extensively investigated, the explainability of the models has been addressed to a very limited extent 29 as also suggested by recent surveys on cyber security attacks. 30,31 Our work tries to fill this gap by proposing a methodological approach to make the machine learning models implemented for detecting phishing websites explainable.…”

Cyber Security System Based on Machine Learning Using Logistic Decision Support Vector

“…Also, it helps to highlight and research several research directions within the scope of the study. Z. Zhang et al, (2022): [12] The author proposed an Explainable Artificial Intelligence (XAI) approach to cyber security applications in current literature. The rapid development of AI has led to widespread use in cyber security fields, including intrusion and malware detection and spam.…”

An efficient cyber security system based on flow-based anomaly detection using Artificial neural network

“…Must be capable of determining optimal feature set validity [5]. [6] The authors proposed a new approach to quantify the relevance and importance of texts describing cyber security. Predefined 'important' texts are analyzed according to their textual similarity with different repositories, and maximum similarity is calculated considering the textual importance.…”