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Backdoor attacks (BA) are an emerging threat to deep neural network classifiers. A classifier being attacked will predict to the attacker's target class when a test sample from a source class is embedded with the backdoor pattern (BP).…

Recent research has successfully demonstrated new types of data poisoning attacks. To address this problem, some researchers have proposed both offline and online data poisoning detection defenses which employ machine learning algorithms to…

The widespread adoption of embedded systems has led to their deployment in critical real-world applications, making them attractive targets for malicious actors. These devices face unique challenges in mitigating vulnerabilities due to…

Modern operating systems provide powerful mandatory access control mechanisms, yet they largely reason about who executes code rather than how execution originates. As a result, processes launched remotely, locally, or by background…

The widespread adoption of Large Language Model (LLM) in commercial and research settings has intensified the need for robust intellectual property protection. Backdoor-based LLM fingerprinting has emerged as a promising solution for this…

Privacy Amplification (PA) is indispensable in Quantum Key Distribution (QKD) post-processing, as it eliminates information leakage to eavesdroppers. Field-programmable gate arrays (FPGAs) are highly attractive for QKD systems due to their…

Micro-Controller Units (MCUs) are widely used in safety-critical systems, making them attractive targets for attacks. This calls for lightweight defenses that remain effective despite software compromise. Control Flow Auditing (CFAud) is…

Privacy-preserving computation techniques like homomorphic encryption (HE) and secure multi-party computation (SMPC) enhance data security by enabling processing on encrypted data. However, the significant computational and CPU-DRAM data…

Modern society is getting accustomed to the Internet of Things (IoT) and Cyber-Physical Systems (CPS) for a variety of applications that involves security-critical user data and information transfers. In the lower end of the spectrum, these…

The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integrity and participant privacy. This study proposes an…

Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of…

Persistent Fault Attack (PFA) is a recently proposed Fault Attack (FA) method in CHES 2018. It is able to recover full AES secret key in the Single-Byte-Fault scenario. It is demonstrated that classical FA countermeasures, such as Dual…

Two-factor authentication (2FA) schemes that rely on a combination of knowledge factors (e.g., PIN) and device possession have gained popularity. Some of these schemes remain secure even against strong adversaries that (a) observe the…

Static Random Access Memory (SRAM) Physically Unclonable Functions (PUFs) make use of intrinsic manufacturing variations in memory cells to derive device-unique responses. Employing such hardware-rooted fingerprints for authentication, this…

The way in which addressing and forwarding are implemented in the Internet constitutes one of its biggest privacy and security challenges. The fact that source addresses in Internet datagrams cannot be trusted makes the IP Internet…

A popular run-time attack technique is to compromise the control-flow integrity of a program by modifying function return addresses on the stack. So far, shadow stacks have proven to be essential for comprehensively preventing return…

LLM developers have imposed technical interventions to prevent fine-tuning misuse attacks, attacks where adversaries evade safeguards by fine-tuning the model using a public API. Previous work has established several successful attacks…

Continuous Authentication (CA) has been proposed as a potential solution to counter complex cybersecurity attacks that exploit conventional static authentication mechanisms that authenticate users only at an ingress point. However, widely…

Remote Direct Memory Access (RDMA) is a key enabler of high-performance systems, offering low latency, high throughput, and reduced CPU overhead by allowing direct memory-to-memory transfers between machines. However, its design bypasses…

Memory errors continue to be a critical concern for programs written in low-level programming languages such as C and C++. Many different memory error defenses have been proposed, each with varying trade-offs in terms of overhead,…