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Explainable machine learning in cybersecurity: A survey
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AI
DOI：10.1002/int.23088.png)
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Abstract
En 中文
Machine learning (ML) techniques are becoming more and more important in cybersecurity, as they can quickly analyse and identify different types of threats from millions of events. In spite of the increasing number of possible applications of machine learning, successful adoption of ML models in cybersecurity still highly relies on the explainability of those models that are used for making predictions. Explanations that support ML model outputs are crucial in cybersecurity-oriented ML applications because people need to get more information from the model than just binary output for analysis. Explainable models help ML developers solve the trust problem for security application predictions by validating model behaviours, diagnosing misclassifications and sometimes automatically patching errors in the target models. Therefore, explainable ML for cybersecurity has become a necessary and important research branch. In this survey, we present the topic of explainable ML in cybersecurity through two general types of explanations: (1) ante hoc explanation, and (2) post hoc explanation, with their methodologies. Specificallly, we systematically review and categorise the state-of-the-art research, and provide comparative studies to help researchers find the optimal solutions to specific cybersecurity problems. We also list open issues in this field to facilitate future studies. This survey will benefit diverse groups of readers from both academia and industries, who want to effectively use ML to solve cybersecurity challenges.
Keywords:
cybersecurity
explainable approaches
explanation
machine learning
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