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Among the available communication systems, vehicular networks are emerging as one of the most promising and yet most challenging instantiations of mobile ad-hoc network technologies. The deployment of such networks in large scale requires…
Blockchain systems face significant scalability challenges due to growing data volumes and increasing transaction demands, necessitating more efficient data structures and verification mechanisms. Verkle trees, a novel data structure…
Vehicular Public Key Infrastructure (VPKI) plays a vital role in ensuring secure and privacy-preserving communication in vehicular ad hoc networks (VANETs). However, current VPKI architectures face significant challenges in terms of…
Post-quantum signature schemes such as ML-DSA-65 produce signatures of 3,309 bytes and public keys of 1,952 bytes over 50 times larger than classical Ed25519. In TLS-authenticated environments like Kubernetes control planes and 5G Core…
Vehicular ad-hoc networks (VANETs) are one of the most important components in Intelligent Transportation System (ITS), which aims to provide secure and efficient communication between vehicles. Safety-critical vehicular communication…
With the increased utilization, the small embedded and IoT devices have become an attractive target for sophisticated attacks that can exploit the devices security critical information and data in malevolent activities. Secure boot and…
The Transport Layer Security (TLS) protocol and its public-key infrastructure (PKI) are widely used in the Internet to achieve secure communication. Validating domain ownership by trusted certification authorities (CAs) is a critical step…
Like any digital certificate, Verifiable Credentials (VCs) require a way to revoke them in case of an error or key compromise. Existing solutions for VC revocation, most prominently Bitstring Status List, are not viable for many use cases…
Virtual network embedding (VNE) algorithm is always the key problem in network virtualization (NV) technology. At present, the research in this field still has the following problems. The traditional way to solve VNE problem is to use…
Modern cloud infrastructure is powered by cluster management systems such as Kubernetes and Docker Swarm. While these systems seek to minimize users' operational burden, the complex, dynamic, and non-deterministic nature of these systems…
Recent work has exposed the vulnerability of computer vision models to vector field attacks. Due to the widespread usage of such models in safety-critical applications, it is crucial to quantify their robustness against such spatial…
This paper outlines the design of `Quest-V', which is implemented as a collection of separate kernels operating together as a distributed system on a chip. Quest-V uses virtualization techniques to isolate kernels and prevent local faults…
High penetrations of intermittent renewable energy resources in the power system require large balancing reserves for reliable operations. Aggregated and coordinated behind-the-meter loads can provide these fast reserves, but represent…
Verifying robustness of neural network classifiers has attracted great interests and attention due to the success of deep neural networks and their unexpected vulnerability to adversarial perturbations. Although finding minimum adversarial…
Future communication networks such as 5G are expected to support end-to-end delivery of services for several vertical markets with diverging requirements. Network slicing is a key construct that is used to provide end to end logical virtual…
This dissertation explores the area of real-time IP networking for embedded devices, especially those with limited computational resources. With the increasing convergence of information and operational technologies in various industries,…
Vision Transformer (ViT) is known to be highly nonlinear like other classical neural networks and could be easily fooled by both natural and adversarial patch perturbations. This limitation could pose a threat to the deployment of ViT in…
The scalability of blockchain technology remains a pivotal challenge, impeding its widespread adoption across various sectors. This study introduces an innovative approach to address this challenge by proposing the adaptive restructuring of…
We develop the first (to the best of our knowledge) provably correct neural networks for a precise computational task, with the proof of correctness generated by an automated verification algorithm without any human input. Prior work on…
In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data. Such certification is imperative for the application of neural networks in safety-critical decision-making and…