CPS: Medium: Secure Computing and Cross-Layer Anomaly Detection in the Internet of Things

这个项目解决了以下问题：“一个相互不信任的设备网络能否在检测异常行为的同时执行弹性推理和计算，尽管它们所感知的数据类型、它们使用的网络技术和它们的计算能力是不同的？”背景是日益普遍的物联网(IoT)，低功率终端用户或传感器依赖边缘设备来处理他们的数据，可能还包括云。然而，物联网带来了一个独特的挑战，即多个层面的极端异构性：数据感知、使用的通信技术(WiFi、蓝牙、Zigbee)和计算能力，使其特别容易受到安全威胁。该项目的目标是开发一个具有弹性的物联网系统和应用程序，重点是在存在威胁的情况下进行分布式推理和计算，从注入异常数据到模拟传感器本身。该系统将通过异类和传感器丰富的园区规模物联网部署进行大规模演示。拟议的试验床提供了一个丰富的平台，通过卡内基梅隆大学的扩展项目，如Engineering@CMU、Spark星期六和Project Ignite，吸引硕士和本科生以及高中生参与。具体地说，该项目旨在为物联网中的安全分布式计算、推理和异常检测开发新的方法基础和跨层系统设计。该方法利用终端用户代理的异构性感知数据及其与边缘设备的交互，提供对广泛类别的拜占庭敌意场景和Sybil攻击的弹性。所提出的分布式算法在数据和感知模型以及代理之间的连通性的广泛条件下，能够保证达到预期的计算和推理目标。为了防御违反拜占庭容错标准假设的Sybil攻击，该项目旨在开发一种基于技术不可知的无线指纹识别解决方案，以检测异常设备和传输。提议的解决方案涉及一种新的深度神经网络设计，以提取贯穿无线电技术的无线指纹。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project tackles the following question: "Can a network of mutually-distrusting devices perform resilient inference and computation while detecting anomalous behaviors despite heterogeneity in the types of data they sense, the networking technologies they use and their computational capabilities?" The context is the increasingly pervasive Internet of Things (IoT) with low-power end users or sensors relying on edge devices to process their data, and possibly the cloud. However, IoT brings forth a unique challenge, namely, the extreme heterogeneity at multiple levels: data sensed, communication technologies used (WiFi, Bluetooth, Zigbee), and computational capabilities, making it particularly vulnerable to security threats. The goal of this project is to develop a resilient IoT system and applications, with a focus on distributed inference and computing in the presence of threats, from injection of anomalous data to impersonation of the sensors themselves. The system will be demonstrated at scale through a heterogeneous and sensor-rich campus-scale IoT deployment. The proposed testbed offers a rich platform to engage Masters and undergraduate students as well as high-schoolers through outreach programs at the Carnegie Mellon University, e.g., Engineering@CMU, SPARK Saturday, and Project Ignite.Specifically, the project aims to develop novel methodological foundations and a cross-layer system design for secure distributed computing and inference and anomaly detection in the IoT. The proposed approach exploits heterogeneous sensing data at the end-user agents and their interaction with edge devices, to provide resilience to broad classes of Byzantine adversarial scenarios and Sybil attacks. The proposed distributed algorithms yield guarantees on attaining desired computation and inference objectives under broad conditions on the data and sensing models and inter-agent connectivity. To defend against Sybil attacks that violate standard assumptions for Byzantine fault tolerance, the project aims to develop a technology-agnostic wireless fingerprinting based solution to detect anomalous devices and transmissions. The proposed solution involves a novel design of a deep neural network to extract wireless fingerprints cutting across radio technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.