【摘要】 边缘智能设备在物联网与安防等场景中广泛应用，其深度学习模型易遭受电磁侧信道攻击。为量化评估模型在此类攻击下的信息泄露情况，构建分层次风险评估框架，从模型家族、层级结构和核心参数3个维度开展分析。结合时频域特征与随机森林算法实现模型家族识别，利用功耗迹线的时序模式与长短期记忆网络完成层级结构及核心参数的自动化推断，并建立信息泄露量化指标以衡量泄露程度。在真实边缘智能设备上，选取9类典型深度学习模型开展实验验证。结果表明，模型家族分类的平均F1分数达95.7%，层级结构恢复精度约93.8%，核心参数识别精度超过90%。研究证实，电磁侧信道可泄露模型多层次信息，且识别精度较高，足以支撑模型克隆及后续攻击行为。该研究为边缘智能设备的侧信道风险认知与防护方案设计提供了量化依据。更多还原

【Abstract】 Edge intelligence devices are widely deployed in the Internet of Things(IoT) and security scenarios, but their deep learning models are vulnerable to electromagnetic side-channel attacks. To quantitatively assess the information leakage of such models under these attacks, a hierarchical risk evaluation framework is proposed, which is analyzed from three dimensions: model family, layer structure, and core parameters. Model family identification is achieved by combining time-frequency features with a random forest algorithm, while the automatic inference of layer structure and core parameters is realized by using the temporal patterns of power traces and a Long Short-Term Memory(LSTM) network. Quantitative indicators are established to measure the information leakage degree. Experiments are conducted on real edge intelligence devices with nine typical deep learning models. The results show that the average F1-score for model family classification reaches 95.7%, the reconstruction accuracy of layer structure is about 93.8%, and the identification accuracy of core parameters exceeds 90%. This study confirms that electromagnetic side channels can leak multi-level model information with high accuracy, and such information is sufficient to support model cloning and subsequent attacks. It provides a quantitative basis for understanding side-channel risks and designing protection schemes for edge intelligence devices.更多还原