IoT environments such as smart homes are susceptible to privacy inference attacks, where attackers can analyze patterns of encrypted network traffic to infer the state of devices and even the activities of people. While most existing attacks exploit ML techniques for discovering such traffic patterns, they underperform on wireless traffic, especially Wi-Fi, due to its heavy noisiness and the packet loss of wireless sniffing. In addition, these approaches commonly target distinguishing chunked IoT event traffic samples, and they fail at effectively tracking multiple events simultaneously. In this work, we propose WiFinger, a fine-grained multi-IoT event fingerprinting approach against noisy traffic. WiFinger turns the traffic pattern classification task into a subsequence matching problem and introduces novel techniques to account for the high time complexity while maintaining high accuracy. In addition, its reliance on training sample volumes reduces efforts for any future fingerprint updates. Experiments demonstrate that WiFinger outperforms existing approaches under practical threat models, with an average recall of 89% (v.s. 49% and 46% respectively) and almost zero false positives for various IoT events.

翻译：智能家居等物联网环境易受隐私推断攻击，攻击者可通过分析加密网络流量的模式来推断设备状态乃至人员活动。现有攻击大多利用机器学习技术发现此类流量模式，但由于无线嗅探的高噪声与丢包特性，这些方法在无线（尤其是Wi-Fi）流量上表现不佳。此外，这些方法通常仅针对分块的物联网事件流量样本进行区分，无法有效同时追踪多个事件。本研究提出WiFinger——一种针对噪声流量的细粒度多物联网事件指纹识别方法。WiFinger将流量模式分类任务转化为子序列匹配问题，并引入创新技术以应对高时间复杂度，同时保持高准确率。此外，该方法对训练样本量的依赖降低了未来指纹更新的工作量。实验表明，WiFinger在实际威胁模型下优于现有方法，对各类物联网事件的平均召回率达到89%（对比方法分别为49%和46%），且误报率近乎为零。