Wang Zihao, Guan Jiale, Wang XiaoFeng, Wang Wenhao, Xing Luyi, Alharbi Fares
Indiana University Bloomington.
Institute of Information Engineering, Chinese Academy of Sciences.
Conf Comput Commun Secur. 2023 Nov;2023:281-295. doi: 10.1145/3576915.3616655. Epub 2023 Nov 21.
Research on side-channel leaks has long been focusing on the information exposure from a single channel (memory, network traffic, power, etc.). Less studied is the risk of learning from multiple side channels related to a target activity (e.g., website visits) even when individual channels are not informative enough for an effective attack. Although the prior research made the first step on this direction, inferring the operations of foreground apps on iOS from a set of global statistics, still less clear are how to determine the maximum information leaks from all target-related side channels on a system, what can be learnt about the target from such leaks and most importantly, how to control information leaks from the whole system, not just from an individual channel. To answer these fundamental questions, we performed the first systematic study on multi-channel inference, focusing on iOS as the first step. Our research is based upon a novel attack technique, called Mischief, which given a set of potential side channels related to a target activity (e.g., foreground apps), utilizes probabilistic search to approximate an optimal subset of the channels exposing most information, as measured by Merit Score, a metric for correlation-based feature selection. On such an optimal subset, an inference attack is modeled as a multivariate time series classification problem, so the state-of-the-art deep-learning based solution, InceptionTime in particular, can be applied to achieve the best possible outcome. Mischief is found to work effectively on today's iOS (16.2), identifying foreground apps, website visits, sensitive IoT operations (e.g., opening the door) with a high confidence, even in an open-world scenario, which demonstrates that the protection Apple puts in place against the known attack is inadequate. Also importantly, this new understanding enables us to develop more comprehensive protection, which could elevate today's side-channel research from suppressing leaks from individual channels to controlling information exposure across the whole system.
长期以来，对侧信道泄漏的研究一直聚焦于单个信道（内存、网络流量、功耗等）的信息暴露。较少被研究的是，即使单个信道提供的信息不足以发动有效攻击，从与目标活动（如网站访问）相关的多个侧信道中获取信息的风险。尽管先前的研究在这个方向上迈出了第一步，即从一组全局统计数据推断iOS上的前台应用操作，但仍不清楚如何确定系统上所有与目标相关的侧信道的最大信息泄漏量，从这些泄漏中可以了解到关于目标的哪些信息，以及最重要的是，如何控制整个系统的信息泄漏，而不仅仅是单个信道的信息泄漏。为了回答这些基本问题，我们首先以iOS为对象，对多信道推理进行了首次系统研究。我们的研究基于一种名为“恶作剧”（Mischief）的新型攻击技术，该技术针对与目标活动（如前台应用）相关的一组潜在侧信道，利用概率搜索来近似找出通过“价值分数”（Merit Score，一种基于相关性的特征选择指标）衡量的、暴露最多信息的信道的最优子集。在这样一个最优子集上，推理攻击被建模为一个多元时间序列分类问题，因此可以应用基于深度学习的最新解决方案，特别是InceptionTime，以实现最佳可能结果。结果发现，“恶作剧”在当今的iOS（16.2）系统上能有效运行，即使在开放世界场景下，也能以高置信度识别前台应用、网站访问、敏感的物联网操作（如开门），这表明苹果针对已知攻击所采取的保护措施并不充分。同样重要的是，这种新认识使我们能够开发更全面的保护措施，这可以将当今的侧信道研究从抑制单个信道的泄漏提升到控制整个系统的信息暴露。
