phd thesis on internet of things

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Master's Theses and Capstones

Internet-of-things (iot) security threats: attacks on communication interface.

Mohammad Mezanur Monjur , University of New Hampshire, Durham

Date of Award

Project type, program or major.

Electrical and Computer Engineering

Degree Name

Master of Science

First Advisor

Second advisor.

Md Shaad Mahmud

Third Advisor

Dongpeng Xu

Internet of Things (IoT) devices collect and process information from remote places and have significantly increased the productivity of distributed systems or individuals. Due to the limited budget on power consumption, IoT devices typically do not include security features such as advanced data encryption and device authentication. In general, the hardware components deployed in IoT devices are not from high end markets. As a result, the integrity and security assurance of most IoT devices are questionable. For example, adversary can implement a Hardware Trojan (HT) in the fabrication process for the IoT hardware devices to cause information leak or malfunctions. In this work, we investigate the security threats on IoT with a special emphasis on the attacks that aim for compromising the communication interface between IoT devices and their main processing host. First, we analyze the security threats on low-energy smart light bulbs, and then we exploit the limitation of Bluetooth protocols to monitor the unencrypted data packet from the air-gapped network. Second, we examine the security vulnerabilities of single-wire serial communication protocol used in data exchange between a sensor and a microcontroller. Third, we implement a Man-in-the-Middle (MITM) attack on a master-slave communication protocol adopted in Inter-integrated Circuit (I2C) interface. Our MITM attack is executed by an analog hardware Trojan, which crosses the boundary between digital and analog worlds. Furthermore, an obfuscated Trojan detection method(ADobf) is proposed to monitor the abnormal behaviors induced by analog Trojans on the I2C interface.

Recommended Citation

Monjur, Mohammad Mezanur, "Internet-of-Things (IoT) Security Threats: Attacks on Communication Interface" (2020). Master's Theses and Capstones . 1388. https://scholars.unh.edu/thesis/1388

Since September 17, 2020

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Home > Student Research, Creative Works, and Publications > Masters Theses and Doctoral Dissertations > 746

Masters Theses and Doctoral Dissertations

Improving iot security through the use of deep learning at the physical layer.

Mohamed Fadul , University of Tennessee at Chattanooga Follow

Committee Chair

Reising, Donald

Committee Member

Sartipi, Mina; Loveless, Thomas Daniel; Weerasena, Lakmali

Dept. of Computational Science

College of Engineering and Computer Science

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

The Internet of Things (IoT) is a heterogeneous network interconnection connecting electronic and electro-mechanical devices to the Internet. The total number of IoT devices is estimated to reach 26.66 billion and is expected to reach 75.4 billion by 2025. Currently, only 30% of the IoT devices employ encryption, which puts the majority of the IoT devices and their underlying infrastructure under risk of attacks by: 1) devices that are wrongly authenticated to access the network specially when digital credentials are transmitted without encryption, and 2) devices that can detect, intercept, and exploit communications between IoT devices. Therefore, more advanced security mechanism are required to secure IoT devices, their corresponding networks, and infrastructure. The Open Systems Interconnect (OSI) stack provides a layered model that governs IoT networks. Based on the OSI stack, the physical (PHY) layer–of each IoT device and associated network–is the first layer exposed to attacks. Traditionally, IoT security techniques are implemented in higher OSI layers, thus these techniques ignore the PHY layer and any potential security advantages it possesses. Due to the demonstrated success of Deep Learning (DL) within the fields of computer vision and image processing, as well as prior research that suggests DL as a viable solution to addressing communications system challenges; this work investigates DL-driven PHY layer security techniques that surpass traditional approaches. The presented work investigates PHY layer security at the encoding and waveform levels. Encoding-based PHY layer security is achieved through an adversarial training and shared-code scheme that leverages DL to redesign a Direct Sequence Spread Spectrum (DSSS) communications system such that it inherently, deliberately, and adaptively prevents an adversary from detecting and reconstructing captured messages. Waveform based PHY layer security is improved through a Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprint process capable of exploiting Specific Emitter Identification (SEI) features that are extracted from waveforms that transverse a Rayleigh fading channel prior to collection. This is achieved through the integration of channel correction prior to DL-based radio identification. The investigated channel correction approaches include traditional and semi supervised learning. The results show that: 1) the DL-based redesign of DSSS encoding achieves featureless signaling that prevents the adversary from reconstructing detected messages, and 2) unsupervised learning based channel correction improves RF-DNA fingerprinting performance by 25% over that of traditional machine learning approaches.

Acknowledgments

First, I would like to acknowledge my wife, Kaley Fadul, my parents, and my three sisters. This work would not have been possible without their support. I am fortunate to have such a loving and supporting family. I would like to express my sincere gratitude to my advisor, Dr. Reising, for the continuous support of my thesis study and related research, and for his patience, motivation, and immense knowledge. His guidance helped me over the whole course of the research. My sincere thanks also goes to the rest of my thesis committee: Dr. Sartipi, Dr. Loveless, and Dr. Weerasena for their patience, understanding, and insightful comments. Very special thanks to team mate Joshua Tyler who was always ready to help with any question I had.

Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy.

Computer security; Internet of things; Deep learning (Machine learning)

Deep Learning; PHY security; Specific Emitter Identification; Machine Learning; Adversarial Training; Spread Spectrum

Document Type

Doctoral dissertations

xi, 113 leaves

http://rightsstatements.org/vocab/InC/1.0/

http://creativecommons.org/licenses/by/4.0/

Date Available

Recommended citation.

Fadul, Mohamed, "Improving IoT security through the use of deep learning at the physical layer" (2022). Masters Theses and Doctoral Dissertations. https://scholar.utc.edu/theses/746

Since April 27, 2022

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