The advancements on the Internet have enabled connecting more devices into this technology every day. This great connectivity has led to the introduction of the internet of things (IoTs) that is a great bed for engagement of all new technologies for computing devices and systems. Nowadays, the IoT devices and systems have applications in many sensitive areas including military systems. These challenges target hardware and software elements of IoT devices and systems. Integration of hardware and software elements leads to hardware systems and software systems in the IoT platforms, respectively. A recent trend for the hardware systems is making them trustworthy and energy efficient. On the other hand, the trend for software systems is making them intelligent and secure. The hardware elements are made energy efficient through implementation of them using emerging transistor and memory technologies. The artificial intelligence (AI) techniques can be utilized in the design and development of software elements. In order to enhance security of the software and the hardware elements, possible threats and countermeasures for them need to be researched and introduced into the community. Globalization of the computing systems and making them Internet-connected introduces diverse set of security threats and malicious activities. These security problems bring detrimental impacts and catastrophic consequences into the networks and systems. In this regard, we address these problems in the IoT world from both hardware and software perspectives. In order to address the emerging security problems in the hardware, we design and develop threats and countermeasures for two different types of Analog to Digital Converter (ADC). This is the first attempt in introducing ADC into the security context. Our findings show that lack of considering the security of ADCs, their performance and functionality can be remarkably degraded due to the payloads of possible attacks. For addressing the ongoing software security problems, we propose: (a) the AI-based software that can help in countering certain attacks; and (b) the techniques for protecting AI-based software against launching attacks on them. We found enhancing the defense systems with AI caters major improvements in detecting malicious information and recognizing the identities. Additionally, we found protection of the AI-based software against functionality manipulative data (a.k.a. adversarial examples) is realized through engaging multiple elements in system training and improving its classification knowledge.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.
Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Taheri, Shayan, "Secure and Trustworthy Hardware and Machine Learning Systems for Internet of Things" (2021). Electronic Theses and Dissertations, 2020-. 569.