关键词: 智能家居, 隐私保护, 深度学习, 对抗样本, 流量特征隐藏

Abstract: The continuous development of deep learning poses new challenges for smart home traffic privacy protection. Traditional traffic privacy protection techniques cannot effectively defend against deep learningbased traffic analysis attacks in blackbox scenarios. To address this, this paper investigates a traffic feature obfuscation method based on adversarial samples. It transforms traffic data into image data, leverages transfer learning to build a device recognition model as the target adversarial model, and uses a generator network to construct adversarial samples based on traffic features. Simultaneously, the network is trained to learn the mapping relationship between regular traffic and adversarial samples while restricting the position and size of perturbations in the adversarial samples. This approach utilizes the model’s transferability to achieve device traffic privacy protection in blackbox scenarios. Experimental results demonstrate that the traffic feature obfuscation method based on adversarial samples can effectively resist attacks from unknown recognition models, thereby safeguarding user privacy.

Key words: smart home, privacy protection, deep learning, adversarial samples, traffic feature obfuscation