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A fundamental problem in cybersecurity and computer science is determining whether a program is free of bugs and vulnerabilities. Fuzzing, a popular approach to discovering vulnerabilities in programs, has several advantages over…
High-throughput imaging workflows, such as Parallel Rapid Imaging with Spectroscopic Mapping (PRISM), generate data at rates that exceed conventional real-time processing capabilities. We present a scalable FPGA-based preprocessing pipeline…
Generating valid test inputs for a program is much easier if one knows the input language. We present first successes for a technique that, given a program P without any input samples or models, learns an input grammar that represents the…
Although software and firmware co-simulation is gaining popularity, it is still not widely used in the FPGA designs. This work presents easy and structured approach for software and firmware co-simulation for bus centric designs. The…
Fuzzing has become one of the most popular techniques to identify bugs in software. To improve the fuzzing process, a plethora of techniques have recently appeared in academic literature. However, evaluating and comparing these techniques…
Smart contracts are susceptible to critical vulnerabilities. Hybrid dynamic analyses, such as concolic execution assisted fuzzing and foundation model assisted fuzzing, have emerged as highly effective testing techniques for smart contract…
There are around 5.3 billion Internet users, amounting to 65.7% of the global population, and web technology is the backbone of the services delivered via the Internet. To ensure web applications are free from security-related bugs, web…
As the Internet of Things (IoT) becomes more embedded within our daily lives, there is growing concern about the risk `smart' devices pose to network security. To address this, one avenue of research has focused on automated IoT device…
Softwarization and virtualization in 5G and beyond require rigorous testing against vulnerabilities and unintended emergent behaviors for critical infrastructure and network security assurance. Formal methods operates efficiently in…
The widespread adoption of microservices has fundamentally transformed how modern software systems are designed, deployed, operated and maintained. However, well-known microservice properties (e.g., dynamic scalability and decentralized…
As quantum processors scale beyond 100 qubits, distinguishing software bugs from stochastic hardware noise becomes a critical diagnostic challenge. We present a neuro-fuzzy framework that addresses this attribution problem by combining…
Verification of microkernels, device drivers, and crypto routines requires analyses at the binary level. In order to automate these analyses, in the last years several binary analysis platforms have been introduced. These platforms share a…
Evaluating architectural ideas on realistic workloads is increasingly challenging due to the prohibitive cost of detailed simulation and the lack of portable sampling tools. Existing targeted sampling techniques are often tied to specific…
This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challenges inherent to multiscale modeling, the proposed approach…
The importance of preventing microarchitectural timing side channels in security-critical applications has surged in recent years. Constant-time programming has emerged as a best-practice technique for preventing the leakage of secret…
Fuzzing is a powerful software testing technique renowned for its effectiveness in identifying software vulnerabilities. Traditional fuzzing evaluations typically focus on overall fuzzer performance across a set of target programs, yet few…
Wireless networks are a common place nowadays and almost all of the modern devices support wireless communication in some form. These networks differ from more traditional computing systems due to the ad-hoc and spontaneous nature of…
Directed fuzzing focuses on automatically testing specific parts of the code by taking advantage of additional information such as (partial) bug stack trace, patches or risky operations. Key applications include bug reproduction, patch…
Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can…
Security vulnerabilities in Internet-of-Things devices, mobile platforms, and autonomous systems remain critical. Traditional mutation-based fuzzers -- while effectively explore code paths -- primarily perform byte- or bit-level edits…