Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
This paper presents an extensive review of literature pertaining to Explainable Artificial Intelligence (XAI) applications in the field of cyber security. The authors underscore the importance of XAI owing to its potential to enhance transparency and trust in Machine Learning (ML) and Deep Learning (DL) models, which have been increasingly employed in cyber security domains like intrusion detection, malware detection, and spam filtering. The conventional signature-based and rule-based strategies are gradually giving way to these AI-based approaches due to the rise of sophisticated cyber attacks. However, ML and DL models often operate as "black-boxes," lacking the transparency required to build user trust and facilitate human understanding of how decisions are made.
The notable absence of comprehensive survey articles concentrating specifically on XAI applications in cyber security forms the motivation for this paper. It seeks to bridge this gap by offering a detailed and updated survey of XAI approaches relevant to various cyber security applications. The paper proposes that integrating XAI into cyber security systems can lead to models that are not only highly accurate but are also explainable, allowing security experts to understand, trust, and manage these models more effectively.
Strong Numerical Results and Bold Claims
The paper identifies a sharp increase in cyber attacks, which were reported to have risen by 29% in 2021. The authors present a framework highlighting the critical role of XAI in building trust and complying with legal standards, such as the European Union's GDPR, which insists on the need for algorithmic decisions to be explainable. They discuss potential methodologies for implementing XAI, categorizing various techniques including intrinsic and post-hoc explanations, model-specific and model-agnostic methods, as well as local and global explanation scopes.
Practical and Theoretical Implications
From a practical standpoint, the implications of research in this field are enormous, as XAI can greatly aid cyber security analysts by providing explanations for how certain predictions and classifications were reached by these AI systems. This capability is crucial when defending against sophisticated attacks, as it allows the analysts to thoroughly understand the rationale behind each decision. This understanding is critical not only for trust and compliance purposes but also for the continuous improvement of these models, fostering an environment where security systems can adapt and evolve in response to new threats.
Theoretically, the survey provides a taxonomy of XAI techniques applicable to cyber security, alongside a comprehensive overview of their existing challenges. The authors argue for a need to develop better frameworks for evaluating the efficacy of XAI models and note that certain popular XAI techniques can often be bypassed by adversarial attacks, suggesting an avenue for further research in model robustness.
Future Developments
Looking ahead, the paper suggests ongoing development in dataset availability and quality, as high-quality datasets are crucial for training more effective and robust AI models. Another area ripe for exploration is the balance between interpretability and accuracy, which is a well-known challenge in deploying AI models but is particularly crucial in high-stakes domains like cyber security. The authors also see the potential for exploring user-centered XAI systems that tailor explanations to the users' level of expertise and context needs.
Conclusion
In summary, this paper provides a meticulous survey of XAI applications in cyber security, addressing the critical need for explainability in AI models used for cyber protection. It highlights key challenges, insights, and suggests future research directions that could guide the academic community towards more transparent, effective, and trustworthy cyber security solutions. This survey serves as a valuable resource for both practitioners and researchers in the field, underscoring the added value XAI brings to cyber security apparatus.