AI 中文总结
本文提出一种基于信息年龄（AoI）的通信网络异常检测框架，利用估计峰值信息年龄（ePAoI）进行特征选择，结合多种机器学习模型，在提升检测性能的同时显著降低计算复杂度。
AI 中文摘要
通信网络中的异常检测仍然是一个挑战，尤其是在时间敏感的环境中，过时信息可能影响系统可靠性。在这项工作中，我们提出了一种异常检测框架，利用信息年龄（AoI）作为识别异常网络行为的时间指标。AoI已被研究用于网络性能优化，然而，其在异常检测中的适用性仍未得到探索。所提出的框架将基于估计的峰值信息年龄（ePAoI）的特征选择技术与多种机器学习模型相结合，作为通信网络的补充指标。评估结果表明，基于ePAoI的特征选择增强了异常检测框架的性能，同时显著降低了计算复杂度。
英文摘要
Anomaly detection in communication networks remains a challenge, particularly in time-sensitive environments where stale information can affect system reliability. In this work, we propose an anomaly detection framework that leverages Age of Information (AoI) as a temporal indicator for identifying abnormal network behaviors. The AoI has been studied for network performance optimization, however, its applicability to anomaly detection remains unexplored. The proposed framework integrates a feature selection technique based on the estimated Peak Age of Information (ePAoI) with multiple machine learning models for communication networks as a complementary indicator. The evaluation results demonstrate that ePAoI-based feature selection enhances the performance of the anomaly detection framework while significantly reducing the computational complexity.
