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Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations, including poisoning attacks via randomized training and evasion attacks via randomized inference. Extending these guarantees to backdoor…
AES-128 encryption is theoretically secure but vulnerable in practical deployments due to timing and fault injection attacks on embedded systems. This work presents a lightweight dual-detection framework combining statistical thresholding…
Graphics Processing Units (GPUs) are widely used by various applications in a broad variety of fields to accelerate their computation but remain susceptible to transient hardware faults (soft errors) that can easily compromise application…
The financial crisis made companies around the world search for cheaper and more efficient solutions to cover their needs in terms of computational power and storage. Their quest came to end with the birth of Cloud Computing…
Software analysis, debugging, and reverse engineering have a crucial impact in today's software industry. Efficient and stealthy debuggers are especially relevant for malware analysis. However, existing debugging platforms fail to address a…
CPU cache is a limited but crucial storage component in modern processors, whereas the cache timing side-channel may inadvertently leak information through the physically measurable timing variance. Speculative execution, an essential…
Increased connectivity and remote reprogrammability/reconfigurability features of embedded devices in current-day power systems (including interconnections between information technology -- IT -- and operational technology -- OT --…
Rising device use and third-party IP integration in semiconductors raise security concerns. Unauthorized access, fault injection, and privacy invasion are potential threats from untrusted actors. Different security techniques have been…
Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of "if" but a matter of "when"…
Microarchitectural attacks represent a challenging and persistent threat to modern processors, exploiting inherent design vulnerabilities in processors to leak sensitive information or compromise systems. Of particular concern is the…
Side channels pose a significant threat to the confidentiality of software systems. Such vulnerabilities are challenging to detect and evaluate because they arise from non-functional properties of software such as execution times and…
Attacks based on side-channel analysis (SCA) pose a severe security threat to modern computing platforms, further exacerbated on IoT devices by their pervasiveness and handling of private and critical data. Designing SCA-resistant computing…
Distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily in distributed deep learning applications when multiple…
Machine learning models based on Deep Neural Networks (DNNs) are increasingly deployed in a wide range of applications ranging from self-driving cars to COVID-19 treatment discovery. To support the computational power necessary to learn a…
Transformers have become the backbone of many Machine Learning (ML) applications, including language translation, summarization, and computer vision. As these models are increasingly deployed in shared Graphics Processing Unit (GPU)…
We present a fully device-resident, multi-GPU architecture for the large-scale computational verification of Goldbach's conjecture. In prior work, a segmented double-sieve eliminated monolithic VRAM bottlenecks but remained constrained by…
The rapid growth of Ethereum has made it more important to quickly and accurately detect smart contract vulnerabilities. While machine-learning-based methods have shown some promise, many still rely on rule-based preprocessing designed by…
It is often difficult to write code that you can ensure will be executed in the right order when programing for parallel compute tasks. Due to the way that today's parallel compute hardware, primarily Graphical Processing Units (GPUs),…
Fine-grained workload and resource balancing is the key to high performance for regular and irregular computations on the GPUs. In this dissertation, we conduct an extensive survey of existing load-balancing techniques to build an…
Recent work has highlighted the risks of intellectual property (IP) piracy of deep learning (DL) models from the side-channel leakage of DL hardware accelerators. In response, to provide side-channel leakage resiliency to DL hardware…