Abstract

Internet of Things (IoT) dominates many functions in the modern world, from sensing and reporting temperature, humidity, and air quality, to controlling and automating homes, commercial buildings, and equipment. However, IoT systems have received scrutiny in recent years due to countless security incidents, which can have physical and even deadly consequences. This research provides a comprehensive assessment of the security of IoT systems and devices, including low-cost microcontroller (MCU) based sensors, cloud services, and Building Automation Systems (BAS). We begin by exploring the current landscape of vulnerabilities and defenses in modern IoT applications. We show that many security needs can be satisfied by modern low-cost MCUs. We discuss how to implement crucial security features in IoT and illustrate use cases through ESP32 MCUs. Next, we investigate vulnerabilities against popular IoT systems and devices. We present a systematic attack model against Message Queuing Telemetry Transport (MQTT) software implementations. We design, implement, and evaluate a fuzz testing framework for MQTT using Markov chain modeling to rigorously exhaust the protocol and identify vulnerabilities. We then demonstrate the plausibility of well-known software attacks on IoT devices. These attacks can be used to remotely steal private keys that are hard coded in the firmware. We also expand our fuzzing research to Building Automation Systems (BAS) devices and software, which are susceptible to similar vulnerabilities as conventional IoT systems and devices. We use dynamic instrumentation and packet analysis to probe the communications between BAS clients and BAS IP interfaces to extract an annotated corpus for mutational fuzzing. Our fuzzer discovered vulnerabilities in various KNX and BACnet devices and software. After exploring these attacks, we discuss how to protect sensitive data in IoT applications using crypto coprocessors. We present a framework for secure key provisioning that protects end users' private keys from software attacks and untrustworthy manufacturers.

Notes

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Graduation Date

2023

Semester

Spring

Advisor

Fu, Xinwen

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Identifier

CFE0009582; DP0027600

URL

https://purls.library.ucf.edu/go/DP0027600

Language

English

Release Date

May 2024

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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