Combating False Data Injection Attacks on Human-Centric Sensing Applications
Combating False Data Injection Attacks on Human-Centric Sensing Applications
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DOI：
10.1145/3534577
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发表时间：
2022-07
影响因子：
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通讯作者：
Jingyu Xin;V. Phoha;Asif Salekin
中科院分区：
文献类型：
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作者：
Jingyu Xin;V. Phoha;Asif Salekin
最近基于机器学习的技术和智能设备嵌入式传感器的流行已经实现了广泛的以人为中心的传感应用。然而，这些应用程序很容易受到虚假数据注入攻击（FDIA）的攻击，这种攻击会使用包含目标特征的伪造数据来改变受害者的部分感官信号。这种伪造信号和有效信号的混合成功地欺骗了连续认证系统 (CAS)，使其接受真实信号。同时，在信号中引入目标特征会误导以人为中心的应用程序生成特定的目标推理；这可能会导致不良后果。本文同时使用两种模式（加速度计、血量脉冲信号）评估 FDIA 对基于传感器的身份验证和以人为中心的传感应用的欺骗效果。我们识别了 FDIA 的变化，例如不同的伪造信号比、平滑和非平滑攻击样本。值得注意的是，我们提出了一种名为 Siamese-MIL 的新型攻击检测框架，该框架通过独特的传感器数据表示来利用 Siamese 神经网络的通用判别能力和多实例学习范例。我们详尽的评估证明了 Siamese-MIL 的实时执行能力以及在不同攻击变体、传感器和应用程序中的高效能。
The recent prevalence of machine learning-based techniques and smart device embedded sensors has enabled widespread human-centric sensing applications. However, these applications are vulnerable to false data injection attacks (FDIA) that alter a portion of the victim's sensory signal with forged data comprising a targeted trait. Such a mixture of forged and valid signals successfully deceives the continuous authentication system (CAS) to accept it as an authentic signal. Simultaneously, introducing a targeted trait in the signal misleads human-centric applications to generate specific targeted inference; that may cause adverse outcomes. This paper evaluates the FDIA's deception efficacy on sensor-based authentication and human-centric sensing applications simultaneously using two modalities - accelerometer, blood volume pulse signals. We identify variations of the FDIA such as different forged signal ratios, smoothed and non-smoothed attack samples. Notably, we present a novel attack detection framework named Siamese-MIL that leverages the Siamese neural networks' generalizable discriminative capability and multiple instance learning paradigms through a unique sensor data representation. Our exhaustive evaluation demonstrates Siamese-MIL's real-time execution capability and high efficacy in different attack variations, sensors, and applications.