Analysis of Explainable Artificial Intelligence Methods in Security Information and Event Management Systems

Analysis of Explainable Artificial Intelligence Methods in Security Information and Event Management Systems

연구 분야: Safety

학회: 2025 Systems of Signals Generating and Processing in the Field of on Board Communications

The present article engages with the ongoing discourse surrounding the implementation of artificial intelligence and explainable artificial intelligence within the domain of information security systems. In the context of escalating cyber threats and mounting data volumes, conventional defense methodologies are proving to be inadequate. Consequently, the integration of artificial intelligence has emerged as a pivotal strategy to enhance the efficacy of threat detection and response processes. The article delves into the intricacies of explainability in artificial intelligence decision-making, exploring explainable artificial intelligence techniques such as local interpretable model-agnostic explanations, shapley additive explanations, gradient-weighted class activation mapping, and layer-wise relevance propagation. These techniques facilitate a more profound comprehension of the rationale behind artificial intelligence models' decisions, thereby enhancing the interpretability of these models. The discourse further encompasses the trade-offs between the accuracy and explainability of artificial intelligence models, along with their potential applications in security information management systems to enhance the efficiency and quality of incident analysis. The integration of explainable artificial intelligence into information security systems has the potential to enhance user confidence in artificial intelligence technologies and optimize human-machine interaction.