This paper proposes the trans-domain digital twin approach to connect heterogeneous domain twins through aligned shared state, explicit coupling, heterogeneous temporal coordination, joint decision-ma…

The paper introduces a threat-oriented digital twinning methodology to enable reproducible and controllable cybersecurity evaluation of autonomous platforms, overcoming limitations in accessing real-w…

This paper proposes a decentralized, blockchain-based protocol using both public and private blockchains to enhance the cyber resilience and security of IoT data transfer for digital building twins an…

The paper proposes a Digital Twin-enabled Simultaneous Learning and Modeling (DT-SLAM) framework to enhance secure communications in UAV-assisted networks against intelligent eavesdropping attacks, ac…

HySecTwin introduces a knowledge-driven digital twin framework that uses semantic modeling and hybrid reasoning to provide explainable, context-aware, and high-speed threat detection for complex Cyber…

This paper presents ArtiTwinSplat, a framework for constructing articulated, photo-realistic digital twins of objects directly from RGB-D videos in real-world environments.

The paper demonstrates that LLMs generate correlated, non-existent character ensembles (ghost couples) whose co-occurrence rates are highly predictable and model-specific, leading to the creation of f…

The paper proposes a Digital Twin (DT)-driven hybrid system that combines deterministic heuristics and constrained Large Language Model (LLM) reasoning to achieve highly accurate and interpretable rea…

The paper proposes a steganographic mechanism, analogous to genetic inheritance, to track the lineage of synthetic information within a cyber ecosystem.

This paper proposes a conceptual framework for digital vehicle forensics (DVF) investigation, addressing the challenges of identifying, preserving, and acquiring digital evidence from modern vehicles.

This paper proposes Twins, a unified continuous token space for multimodal models using ViT and VAE features, and addresses optimization imbalance with a focal regression objective.

The paper introduces Test-Driven Forensics, an approach that treats forensic expectations as executable tests to detect and measure the degradation of repeatability and confidence in digital forensic…

This paper analyzes the performance of agentic LLM systems in complex binary reverse engineering, identifying key limitations such as handling obfuscation and token constraints, and proposing future d…

DEMUX is a novel framework that addresses the challenge of multi-tab website fingerprinting by treating the interleaved traffic as a demixing problem, achieving state-of-the-art performance in complex…

The paper demonstrates that off-the-shelf image diffusion models, like Stable Diffusion, can be repurposed to generate synthetic structured data, posing a threat of ground truth drift in closed eviden…

This study analyzes global usage patterns of generative AI among early adopters, finding that usage varies significantly by country income, with schooling being the primary use in low-income countries…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…