相关论文: Tech-ASan: Two-stage check for Address Sanitizer
Existing techniques to ensure functional correctness and hardware trust during pre-silicon verification face severe limitations. In this work, we systematically leverage two key ideas: 1) Symbolic Quick Error Detection (Symbolic QED or…
Safety-critical applications like autonomous vehicles and industrial IoT are adopting semantic communication (SemCom) systems using deep neural networks to reduce bandwidth and increase transmission speed by transmitting only task-relevant…
Artificial Neural Networks (ANNs) have demonstrated remarkable utility in various challenging machine learning applications. While formally verified properties of their behaviors are highly desired, they have proven notoriously difficult to…
In real-world applications, it is challenging to build a speaker verification system that is simultaneously robust against common threats, including spoofing attacks, channel mismatch, and domain mismatch. Traditional automatic speaker…
Specialized hardware accelerators have been designed and employed to maximize the performance efficiency of Spiking Neural Networks (SNNs). However, such accelerators are vulnerable to transient faults (i.e., soft errors), which occur due…
In the last decade it became a common practice to formalise software requirements to improve the clarity of users' expectations. In this work we build on the fact that functional requirements can be expressed in temporal logic and we…
Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn generalized underlying structure. In this work, we harness…
Most programming languages use monitors with explicit signals for synchronization in shared-memory programs. Requiring program- mers to signal threads explicitly results in many concurrency bugs due to missed notifications, or notifications…
Backdoor attacks pose severe security threats to deep neural networks by embedding malicious triggers that force misclassification. While machine unlearning techniques can remove backdoor behaviors, current methods lack transparency and…
The timing characteristics of cache, a high-speed storage between the fast CPU and the slowmemory, may reveal sensitive information of a program, thus allowing an adversary to conduct side-channel attacks. Existing methods for detecting…
Trusted execution environments (TEEs) for machine learning accelerators are indispensable in secure and efficient ML inference. Optimizing workloads through state-space exploration for the accelerator architectures improves performance and…
Growing interest in automatic speaker verification (ASV)systems has lead to significant quality improvement of spoofing attackson them. Many research works confirm that despite the low equal er-ror rate (EER) ASV systems are still…
Voice anonymization masks vocal traits while preserving linguistic content, which may still leak speaker-specific patterns. To assess and strengthen privacy evaluation, we propose a dual-stream attacker that fuses spectral and…
The Cyber threats exposure has created worldwide pressure on organizations to comply with cyber security standards and policies for protecting their digital assets. Vulnerability assessment (VA) and Penetration Testing (PT) are widely…
The development of a system vulnerability analysis tool (SVAT) for complex mission critical systems (CMCS) produced the software for operation and network attack results review (SONARR). This software builds upon the Blackboard Architecture…
The malware booming is a cyberspace equal to the effect of climate change to ecosystems in terms of danger. In the case of significant investments in cybersecurity technologies and staff training, the global community has become locked up…
The increasing misuse of AI-generated texts (AIGT) has motivated the rapid development of AIGT detection methods. However, the reliability of these detectors remains fragile against adversarial evasions. Existing attack strategies often…
In the application of IC design for microprocessors, there are often demands for optimizing the implementation of datapath circuits, on which various arithmetic operations are performed. Combinational equivalence checking (CEC) plays an…
Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None…
Artificial intelligence is observed to age not through chronological time but through structural asymmetries in memory performance. In large language models, semantic cues such as the name of the day often remain stable across sessions,…