Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Применение объяснимого искусственного интеллекта в кибербезопасности: современное состояние исследований
2022-01-01
SCID: 54.1/dxujc9ey
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Explainable Artificial Intelligence (XAI)cyber securityintrusion detectionmalware detectionspam filtering
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Abstract (AI)
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 and Deep Learning 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 Machine Learning-based techniques and Deep Learning-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.
Key Findings
1
AI (ML and DL) is widely used in cyber security tasks (intrusion detection, malware detection, spam filtering) but is largely deployed as black-box models lacking interpretability.
2
Applying XAI to cyber security is essential to create explainable models that maintain high accuracy while enabling human comprehension, trust, and control.
3
Lack of transparency and interpretability in AI-based cyber security reduces user confidence and hinders management of complex, diverse cyber attacks.
4
There is a notable research gap: no prior survey focuses specifically on XAI applications in cyber security, motivating this comprehensive, up-to-date review.
5
This work provides the first clear roadmap for navigating XAI literature in the context of cyber security applications.
Research Object
Explainable Artificial Intelligence (XAI) methods applied to cyber security systems and tasks (e.g., intrusion detection, malware detection, spam filtering)
Research Subject
The transparency, interpretability, and applicability of XAI approaches for improving trust, explainability, and maintenance of AI-based cyber security models while preserving detection accuracy
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2022-01-01
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