标题： 隐于显眼之处：一种增强隐私的物联网流量伪装框架 显示英文标题

标题： Hiding in Plain Sight: An IoT Traffic Camouflage Framework for Enhanced Privacy

摘要： 物联网（IoT）设备的快速增长给隐私带来了重大挑战，尤其是在网络流量分析技术不断演进的背景下。 尽管加密保护了数据内容，但诸如数据包大小和时间等流量属性可能会泄露有关用户和设备的敏感信息。 现有的单一技术混淆方法，如数据包填充，由于其可预测性，在动态环境如智能家居中往往效果不佳，容易受到基于机器学习的攻击。 本文介绍了一种多技术混淆框架，旨在通过破坏流量分析来增强隐私。 该框架利用了六种技术——填充、异或填充、位移填充、固定大小填充、分片和延迟随机化——以有效隐藏流量模式。 在三个公开数据集上的评估表明，分类器性能指标（包括准确率、精确率、召回率和F1分数）显著降低。 我们通过在混淆流量上重新训练和微调神经网络分类器来评估框架对对抗策略的鲁棒性。 结果表明分类器性能明显下降，这突显了框架对自适应攻击的韧性。 此外，我们评估了通信和系统性能，结果显示更高的混淆级别可以提高隐私性，但可能会增加延迟和通信开销。 显示英文摘要

摘要： The rapid growth of Internet of Things (IoT) devices has introduced significant challenges to privacy, particularly as network traffic analysis techniques evolve. While encryption protects data content, traffic attributes such as packet size and timing can reveal sensitive information about users and devices. Existing single-technique obfuscation methods, such as packet padding, often fall short in dynamic environments like smart homes due to their predictability, making them vulnerable to machine learning-based attacks. This paper introduces a multi-technique obfuscation framework designed to enhance privacy by disrupting traffic analysis. The framework leverages six techniques-Padding, Padding with XORing, Padding with Shifting, Constant Size Padding, Fragmentation, and Delay Randomization-to obscure traffic patterns effectively. Evaluations on three public datasets demonstrate significant reductions in classifier performance metrics, including accuracy, precision, recall, and F1 score. We assess the framework's robustness against adversarial tactics by retraining and fine-tuning neural network classifiers on obfuscated traffic. The results reveal a notable degradation in classifier performance, underscoring the framework's resilience against adaptive attacks. Furthermore, we evaluate communication and system performance, showing that higher obfuscation levels enhance privacy but may increase latency and communication overhead.