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Cache side channel attacks are increasingly alarming in modern processors due to the recent emergence of Spectre and Meltdown attacks. A typical attack performs intentional cache access and manipulates cache states to leak secrets by…
Adversarial audio attacks can be considered as a small perturbation unperceptive to human ears that is intentionally added to the audio signal and causes a machine learning model to make mistakes. This poses a security concern about the…
Multi-Variant Execution Environments (MVEEs) are a promising technique to protect software against memory corruption attacks. They transparently execute multiple, diversified variants (often referred to as replicae) of the software…
Remote Attestation (RA) allows a trusted entity (verifier) to securely measure internal state of a remote untrusted hardware platform (prover). RA can be used to establish a static or dynamic root of trust in embedded and cyber-physical…
Homomorphic encryption (HE) enables computations directly on encrypted data, offering strong cryptographic guarantees for secure and privacy-preserving data storage and query execution. However, despite its theoretical power, practical…
Microcode is an abstraction layer used by modern x86 processors that interprets user-visible CISC instructions to hardware-internal RISC instructions. The capability to update x86 microcode enables a vendor to modify CPU behavior in-field,…
Digital substations have significantly enhanced power grid protection by replacing traditional copper wiring with fiber-optic communication and integrating IEC 61850-compliant Intelligent Electronic Devices (IEDs), resulting in greater…
Siemens produce a range of industrial human machine interface (HMI) screens which allow operators to both view information about and control physical processes. For scenarios where an operator cannot physically access the screen, Siemens…
In this paper, we propose a block-wise image transformation method with a secret key for support vector machine (SVM) models. Models trained by using transformed images offer a poor performance to unauthorized users without a key, while…
FPGAs are now used in public clouds to accelerate a wide range of applications, including many that operate on sensitive data such as financial and medical records. We present ShEF, a trusted execution environment (TEE) for cloud-based…
In the era of the internet and smart devices, the detection of malware has become crucial for system security. Malware authors increasingly employ obfuscation techniques to evade advanced security solutions, making it challenging to detect…
Large Language Model (LLM) agents are increasingly used to automate complex workflows, but integrating untrusted external data with privileged execution exposes them to severe security risks, particularly direct and indirect prompt…
Modern processors are increasingly featuring multiple cores, as well as support for hardware virtualization. While these processors are common in desktop and server-class computing, they are less prevalent in embedded and real-time systems.…
Prime numbers are fundamental in number theory and play a significant role in various areas, from pure mathematics to practical applications, including cryptography. In this contribution, we introduce a multithreaded implementation of the…
A substantial body of research has focused on formalising what constitutes a ``secure'' messaging system, recognising that end-to-end encryption alone is insufficient to capture the full range of security, privacy, and usability properties…
Verifiable Secret-Sharing (VSS) is a fundamental primitive in secure distributed computing. It is used as a building block in several distributed computing tasks, such as Byzantine agreement and secure multi-party computation. In this…
Secure aggregation usually aims at securely computing the sum of the inputs from $K$ users at a server. Noticing that the sum might inevitably reveal information about the inputs (when the inputs are non-uniform) and typically the users…
Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with active learning to…
The use of trusted hardware has become a promising solution to enable privacy-preserving machine learning. In particular, users can upload their private data and models to a hardware-enforced trusted execution environment (e.g. an enclave…
This paper proposes a novel secure live virtual machine migration framework by using a virtual trusted platform module instance to improve the integrity of the migration process from one virtual machine to another on the same platform. The…