arXiv subjects

Jack Miskelly

Publications and source records attributed to Jack Miskelly.

4 recordsLinked to original sources

arXiv2026

Quantifying Memory Cells Vulnerability for DRAM Security

Dynamic Random Access Memory (DRAM) is pervasive in computer systems. Cell vulnerabilities caused by unintended phenomena (forced retention failure, latency alteration, rowhammer and rowpress) lead to unintended bit flips in memory. These phenomena have been explored as attacks to violate data integrity and confidentiality during normal operation, but also exploited as a benefit in security systems as a method to generate random secret keys and unique device fingerprints (e.g. Physically Unclonable Functions). In both cases, attackers may wish to exploit knowledge of individual cell flip vulnerability to predict the current/future data contents of a set of cells, which can be utilised to break security systems. In this work, we develop a quantitative, cell-level circuit framework that models DRAM vulnerability directly from its physical charge leakage and disturbance pathways. By linking these device-layer behaviours to system-level security properties, our framework enables systematic evaluation of DRAM with respect to volatility (retention), integrity (disturbance-induced modification), and confidentiality (pattern-dependent leakage). We further demonstrate how the framework can be applied to well-known failure modes, revealing non-uniform and context-dependent vulnerability patterns. This work provides both theoretical foundations and practical evaluation tools for evaluating the suitability of DRAM use within security applications.

cs.CR↗

arXiv2024

PhenoAuth: A Novel PUF-Phenotype-based Authentication Protocol for IoT Devices

Physical Unclonable Functions (PUFs) have been shown to be a highly promising solution for enabling high security systems tailored for low-power devices. Commonly, PUFs are utilised to generate cryptographic keys on-the-fly, replacing the need to store keys in vulnerable, non-volatile memories. Due to the physical nature of PUFs, environmental variations cause noise, manifesting themselves as errors which are apparent in the initial PUF measurements. This necessitates expensive active error correction techniques which can run counter to the goal of lightweight security. ML-based techniques for authenticating noisy PUF measurements were explored as an alternative to error correction techniques, bringing about the concept of a PUF Phenotype, where PUF identity is considered as a structure agnostic representation of the PUF, with relevant noise encoding. This work proposes a full noise-tolerant authentication protocol based on the PUF Phenotype concept and methodology for an Internet-of-Things (IoT) network, demonstrating mutual authentication and forward secrecy in a setting suitable for device-to-device communication. Upon conducting security and performance analyses, it is evident that our proposed scheme demonstrates resilience against various attacks compared to the currently existing PUF protocols.

cs.CR↗

arXiv2024

Attacking Delay-based PUFs with Minimal Adversary Model

Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelling-resistant PUFs and devising machine learning attacks against them. This dichotomy often results in exaggerated concerns and overconfidence in PUF security, primarily because there lacks a universal tool to gauge a PUF's security. In many scenarios, attacks require additional information, such as PUF type or configuration parameters. Alarmingly, new PUFs are often branded `secure' if they lack a specific attack model upon introduction. To impartially assess the security of delay-based PUFs, we present a generic framework featuring a Mixture-of-PUF-Experts (MoPE) structure for mounting attacks on various PUFs with minimal adversarial knowledge, which provides a way to compare their performance fairly and impartially. We demonstrate the capability of our model to attack different PUF types, including the first successful attack on Heterogeneous Feed-Forward PUFs using only a reasonable amount of challenges and responses. We propose an extension version of our model, a Multi-gate Mixture-of-PUF-Experts (MMoPE) structure, facilitating multi-task learning across diverse PUFs to recognise commonalities across PUF designs. This allows a streamlining of training periods for attacking multiple PUFs simultaneously. We conclude by showcasing the potent performance of MoPE and MMoPE across a spectrum of PUF types, employing simulated, real-world unbiased, and biased data sets for analysis.

cs.CR↗

arXiv2022

PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning

As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure system designers, they have been shown to be vulnerable to various sophisticated attacks - most notably Machine Learning (ML) based modelling attacks (ML-MA) which attempt to digitally clone the PUF behaviour and thus undermine their security. More recent ML-MA have even exploited publicly known helper data required for PUF error correction in order to predict PUF responses without requiring knowledge of response data. In response to this, research is beginning to emerge regarding the authentication of PUF devices with the assistance of ML as opposed to traditional PUF techniques of storage and comparison of pre-known Challenge-Response pairs (CRPs). In this article, we propose a classification system using ML based on a novel `PUF-Phenotype' concept to accurately identify the origin and determine the validity of noisy memory derived (DRAM) PUF responses as an alternative to helper data-reliant denoising techniques. To our best knowledge, we are the first to perform classification over multiple devices per model to enable a group-based PUF authentication scheme. We achieve up to 98\% classification accuracy using a modified deep convolutional neural network (CNN) for feature extraction in conjunction with several well-established classifiers. We also experimentally verified the performance of our model on a Raspberry Pi device to determine the suitability of deploying our proposed model in a resource-constrained environment.

cs.CR↗