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

摘要ECCS-1744129标题：Eager：Cyberattacks on Commercial IoT Networks Estimating Object Position非技术描述：虽然互联网已经存在多年，但将传感和控制技术集成到互联网中以产生所谓的物联网（IoT）仍然非常不成熟，并带来了危险的新的未解决的安全问题。 例如，已经观察到对汽车处理器的网络攻击。汽车制造商正在开发关键系统，旨在融合来自多个复杂传感器的数据，以确保自动驾驶汽车避免与人和动物发生碰撞。拟议的项目将寻求开发一个完整的商业物联网/传感器对象位置估计网络攻击缓解和影响的理论，其基础是：（1）严格证明在一些合理的假设下可以识别受攻击的传感器，以及（2）基于严格估计理论的分析受攻击系统可以实现的可能性能范围。新的理论应该导致技术，以防止对智能家居，智能建筑，智能工厂和日常生活中依赖的其他商业物联网/传感器系统的网络攻击。申请资金的大部分将用于支持研究生。教育研究生和本科生，从代表性不足的群体，在这些重要的跨学科领域将继续下去。这个研究项目，类和利哈伊的综合电力网络（INE）跨学科研究计划之间的协调是与这个项目相关的更广泛的影响。研究成果将纳入目前和未来的利哈伊类。 课堂笔记可能会演变成一个短期的课程，并可能是一本关于传感系统安全的书，以提供一个很大的教育影响。技术说明：对于正在考虑的对象定位问题，研究将精确地描述在每个传感器上的观测如何约束可能的对象位置时，没有攻击。 该研究还将描述增加每个传感器的观测数量和不存在攻击时传感器数量的影响。 此外，研究将分析交叉约束在多个传感器上的影响，以确切地显示在没有攻击的情况下，交叉约束如何提供包含对象位置的严格较小的集合。 大偏差分析将被用来证明，这种方法可以正确地定位目标时，在每个传感器有足够数量的观察具有很高的概率。 观察结果较少的病例将使用适当的界限进行分析，这些界限对于有限数量的观察结果是准确的。这些分析方法将在攻击下使用，以表明显著的攻击将驱动空集合的约束。大偏差分析将被用来建立这种方法可以正确地识别受攻击的传感器，在一些合理的假设下，当有足够数量的观察在每个传感器具有高概率。 观察结果较少的病例将使用适当的界限进行分析，这些界限对于有限数量的观察结果是准确的。

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.