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

CPS：中：物联网中的安全计算和跨层异常检测

基本信息

批准号：
1837607
负责人：
Soummya Kar
金额：
$ 100万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837607&HistoricalAwards=false
关键词：
CPS Medium Secure Computing Cross

项目摘要

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.

该项目解决了以下问题：“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，尽管它们感知的数据类型、使用的网络技术和计算能力存在异构性？”背景是日益普及的物联网 (IoT)，低功耗最终用户或传感器依赖边缘设备（可能还包括云）来处理数据。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：感知的数据、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有弹性的物联网系统和应用程序，重点是存在威胁时的分布式推理和计算，从注入异常数据到模拟传感器本身。该系统将通过异构且传感器丰富的校园规模物联网部署进行大规模演示。拟议的测试平台提供了一个丰富的平台，通过卡内基梅隆大学的外展项目（例如 Engineering@CMU、SPARK Saturday 和 Project Ignite）吸引硕士生、本科生以及高中生。具体而言，该项目旨在开发新颖的方法论物联网中安全分布式计算、推理和异常检测的基础和跨层系统设计。所提出的方法利用最终用户代理的异构传感数据及其与边缘设备的交互，为广泛的拜占庭对抗场景和 Sybil 攻击提供弹性。所提出的分布式算法保证了在数据和传感模型以及代理间连接的广泛条件下实现所需的计算和推理目标。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与技术无关的基于无线指纹的解决方案来检测异常设备和传输。所提出的解决方案涉及深度神经网络的新颖设计，以提取跨无线电技术的无线指纹。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data

Forecaster：用于预测空间和时间相关数据的图形转换器

DOI：
10.3233/faia200231
发表时间：
2019-09-09
期刊：
ArXiv
影响因子：
0
作者：
Y. Li;J. Moura
通讯作者：
J. Moura

Quick (and Dirty) Aggregate Queries on Low-Power WANs

低功耗 WAN 上的快速（且脏）聚合查询

DOI：
10.1109/ipsn48710.2020.00031
发表时间：
2020-04
期刊：
2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN
影响因子：
0
作者：
Gadre, Akshay;Yi, Fan;Rowe, Anthony;Iannucci, Bob;Kumar, Swarun
通讯作者：
Kumar, Swarun

Resilient Distributed Field Estimation

弹性分布式场估计