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

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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.