Securing Personal IoT Platforms through Systematic Analysis and Design

Our homes, hospitals, cities, and industries are being enhanced with devices that have computational and networking capabilities. This emerging network of connected devices, or Internet of Things (IoT), promises better safety, enhanced management of patients, improved energy efficiency, and optimized manufacturing processes. Although there are many such benefits, security vulnerabilities in these systems can lead to user dissatisfaction (e.g., from random bugs), privacy violation (e.g., from stolen information), monetary loss (e.g., denial-of-service attacks or ``ransomware''), or even loss of life (e.g., from malicious actors manipulating critical processes in a hospital). Security design flaws may manifest at several layers of the IoT software/hardware stack. This work focuses on design flaws that arise in IoT platforms---software systems that manage devices, data analysis results and control logic. Specifically, we show that empirical security-oriented analyses of personal IoT platforms lead to: (1) an understanding of design flaws that can be leveraged in long-range and device-independent attacks; (2) the development of security mechanisms that limit the potential for these attacks. Concretely, we contribute empirical analyses for two categories of personal IoT platforms---Hub-Based (Samsung SmartThings), and Cloud-First (If-This-Then-That). Our analyses reveal overprivilege as a main enabler for attacks, and we propose a set of information flow control techniques (FlowFence and Decoupled-IFTTT) to manage privilege better in these platforms, therefore reducing the potential for attacks.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137083/1/earlence_1.pd

Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (distance, velocity etc.) of relevant traffic objects based on sensor measurements. The tracking output of KF is often fed into downstream logic to produce alerts, which will then be used by human drivers to make driving decisions in near-collision scenarios. In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning. Our attack goal is to negatively affect human braking decisions by causing KF to output incorrect state estimations that lead to false or delayed alerts. We accomplish this by sequentially manipulating measure ments fed into the KF, and propose a novel Model Predictive Control (MPC) approach to compute the optimal manipulation. Via experiments conducted in a simulated driving environment, we show that the attacker is able to successfully change FCW alert signals through planned manipulation over measurements prior to the desired target time. These results demonstrate that our attack can stealthily mislead a distracted human driver and cause vehicle collisions.Comment: Accepted by AAAI2