Date of Award
Fall 10-2022
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
School
Computing Sciences and Computer Engineering
Committee Chair
Ahmed Sherif
Committee Chair School
Computing Sciences and Computer Engineering
Committee Member 2
Ras B. Pandey
Committee Member 2 School
Mathematics and Natural Sciences
Committee Member 3
Dia Ali
Committee Member 3 School
Computing Sciences and Computer Engineering
Committee Member 4
Chaoyang Zhang
Committee Member 4 School
Computing Sciences and Computer Engineering
Committee Member 5
Amer Dawoud
Committee Member 5 School
Computing Sciences and Computer Engineering
Abstract
Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC.
In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our vision of a layer-based approach to thoroughly study state-of-the-art literature in the realm of AVs. Particularly, we examined some cyber-attacks and compared their promising mitigation strategies from our perspective. Then, we focused on two security issues involving AVCC: software protection and authentication.
For the first problem, our concern is protecting client’s programs executed on remote AVCC resources. Such a usage scenario is susceptible to information leakage and reverse-engineering. Hence, we proposed compiler-based obfuscation techniques. What distinguishes our techniques, is that they are generic and software-based and utilize the intermediate representation, hence, they are platform agnostic, hardware independent and support different high level programming languages. Our results demonstrate that the control-flow of obfuscated code versions are more complicated making it unintelligible for timing side-channels.
For the second problem, we focus on protecting AVCC from unauthorized access or intrusions, which may cause misuse or service disruptions. Therefore, we propose a strong privacy-aware authentication technique for users accessing AVCC services or vehicle sharing their resources with the AVCC. Our technique modifies robust function encryption, which protects stakeholder’s confidentiality and withstands linkability and “known-ciphertexts” attacks. Thus, we utilize an authentication server to search and match encrypted data by performing dot product operations. Additionally, we developed another lightweight technique, based on KNN algorithm, to authenticate vehicles at computationally limited charging stations using its owner’s encrypted iris data. Our security and privacy analysis proved that our schemes achieved privacy-preservation goals. Our experimental results showed that our schemes have reasonable computation and communications overheads and efficiently scalable.
ORCID ID
https://orcid.org/0000-0002-6474-6173
Recommended Citation
Hataba, Muhammad, "Software Protection and Secure Authentication for Autonomous Vehicular Cloud Computing" (2022). Dissertations. 2071.
https://aquila.usm.edu/dissertations/2071
Included in
Computational Engineering Commons, Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons