Eager: Cyberattacks on Commercial IoT Networks Estimating Large Dimension Parameters for Big Data

Eager：对商业物联网网络的网络攻击估计大数据的大维度参数

基本信息

批准号：
1744129
负责人：
Rick Blum
金额：
$ 20万
依托单位：
Lehigh University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1744129&HistoricalAwards=false
关键词：
Eager Cyberattacks Commercial IoT Networks

项目摘要

Abstract ECCS -1744129Title: Eager: Cyberattacks on Commercial IoT Networks Estimating Object Position Non-technical description: While the internet has been available for many years, the integration of sensing and control technology into the internet to yield what is being called the Internet of Things (IoT) is still very immature and brings dangerous new unaddressed security problems. For example, cyberattacks on automotive processors have already been observed. Car manufacturers are developing critical systems aimed at fusing data from several complex sensors to ensure self-driving automobiles avoid collisions with people and animals. The proposed project will seek to develop a complete theory of commercial IoT/sensor object location estimation network attack mitigation and impact based on: (1) rigorous proofs that the attacked sensors can be identified under some reasonable assumptions and (2) rigorous estimation theory-based analysis of the possible range of performance that the attacked system can achieve. The new theory should lead to technology to protect against cyber attacks on smart homes, smart buildings, smart factories and other commercial IoT/sensor systems relied upon in daily lives. The major portion of the requested funds will go towards supporting graduate students. Educating graduate and undergraduate students, from under represented groups, in these important cross-disciplinary areas will be pursued. Coordination between this research project, classes and Lehigh's Integrated Networks for Electricity (INE) interdisciplinary research initiative is broader impact associated with this project. Research results will be incorporated into current and future Lehigh classes. Class notes might evolve into a short course, and possibly a book on security of sensing systems, to provide a large educational impact.Technical description: For the object localization problems under consideration, the research will characterize precisely on how observations at each sensor constrain the possible object position when no attacks are present. The research will also characterize the impact of increasing the number of observations per sensor and the number of sensors when no attacks are present. Further, the research will analyze the impact of intersecting constraints at multiple sensors to show exactly how the intersected constraints provide a strictly smaller set containing the object location under the case of no attacks. Large deviation analysis will be used to demonstrate that this approach can properly localize the target with high probability when a sufficient number of observations are available at each sensor. Cases with fewer observations will be analyzed using appropriate bounds that are accurate with a finite number of observations. These analytical approaches will be employed under attacks to show that significant attacks will drive the intersected constraints to the empty set. Large deviation analysis will be used to establish that this approach can properly identify attacked sensors, under some reasonable assumptions, when a sufficient number of observations are available at each sensor with high probability. Cases with fewer observations will be analyzed using appropriate bounds that are accurate with a finite number of observations.

摘要ECC -1744129TITLE：急切：商业物联网网络上的网络攻击估计对象位置非技术描述：虽然互联网已经可以使用了很多年了，但将传感和控制技术的整合到互联网中以产生所谓的事物Internet（IoT）（IoT）仍然非常不成熟，并带来了危险的新的未犯下的无压力的安全问题。例如，已经观察到汽车处理器上的网络攻击。汽车制造商正在开发旨在融合几个复杂传感器数据的关键系统，以确保自动驾驶汽车避免与人和动物发生碰撞。拟议的项目将寻求制定商业物联网/传感器对象位置估计网络攻击缓解和基于影响的完整理论：（1）严格的证据，即可以在某些合理的假设下确定受攻击的传感器，以及（2）基于严格的估计理论对攻击系统可以实现的可能性范围的分析。新理论应导致技术以防止对智能家居，智能建筑，智能工厂和其他商业物联网/传感器系统的网络攻击。要求的资金的主要部分将用于支持研究生。将在这些重要的跨学科领域中教育研究生和本科生，来自代表的群体。该研究项目，班级和Lehigh的电力综合网络（INE）跨学科研究计划之间的协调是与该项目相关的更大影响。研究结果将纳入当前和未来的Lehigh课程中。课堂笔记可能会演变成简短的课程，甚至可能是一本关于传感系统安全性的书，以提供巨大的教育影响。技术描述：对于正在考虑的对象定位问题，该研究将准确地特征在于每个传感器的观察值如何在不存在攻击的情况下如何限制可能的对象位置。该研究还将表征增加每个传感器观测值数量以及没有攻击时传感器数量的影响。此外，该研究将分析多个传感器上相交约束的影响，以准确显示相交的约束如何提供一个严格的较小集合，该集合在无攻击的情况下包含对象位置。大型偏差分析将用于证明，当每个传感器都有足够数量的观测值时，这种方法可以很高的可能性正确地定位目标。将使用适当的界限分析具有较少观察结果的病例，这些范围具有准确的观测值。这些分析方法将在攻击下采用，以表明重大攻击将推动相交的约束对空集的限制。大型偏差分析将用于确定这种方法可以在某些合理的假设下正确地识别受攻击的传感器，而在每个传感器上有足够数量的观测值都具有很高的概率。将使用适当的界限分析具有较少观察结果的病例，这些范围具有准确的观测值。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Ordered Gradient Approach for Communication-Efficient Distributed Learning

用于高效通信的分布式学习的有序梯度方法

DOI：
10.1109/spawc48557.2020.9153887
发表时间：
2020
期刊：
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC
影响因子：
0
作者：
Chen, Yicheng;Sadler, Brian M.;Blum, Rick S.
通讯作者：
Blum, Rick S.

Artificial Noise-Aided MIMO Physical Layer Authentication With Imperfect CSI

DOI：
10.1109/tifs.2021.3050599
发表时间：
2021-01-01
期刊：
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
影响因子：
6.8
作者：
Perazzone, Jake Bailey;Yu, Paul L.;Blum, Rick S.
通讯作者：
Blum, Rick S.

Optimal Quickest Change Detection in Sensor Networks Using Ordered Transmissions

使用有序传输的传感器网络中的最佳最快变化检测

DOI：
10.1109/spawc48557.2020.9154270
发表时间：
2020
期刊：
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC
影响因子：
0
作者：
Chen, Yicheng;Blum, Rick S.;Sadler, Brian M.
通讯作者：
Sadler, Brian M.

A Fundamental Limitation on Maximum Parameter Dimension for Accurate Estimation With Quantized Data