Abstract As Artificial Intelligence (AI) rapidly advances and integrates into various domains,
cybersecurity emerges as a critical field grappling with both the benefits and pitfalls of AI …

In this paper, we improve the robustness of Machine Learning (ML) classifiers against
training-time attacks by linking the risk of training data being tampered with to the …

This paper presents a reputation-based threat mitigation framework that defends potential
security threats in electroencephalogram (EEG) signal classification during model …

Ontologies have the potential to play an important role in the cybersecurity landscape as
they are able to provide a structured and standardized way to semantically represent and …

With the recent advances in 5G and 6G communications and the increasing need for
immersive interactions due to pandemic, new use cases such as All-Senses meeting are …

The rapid integration of Large Language Models (LLMs) across diverse sectors has marked
a transformative era, showcasing remarkable capabilities in text generation and problem …

AI applications bear inherent risks in various risk dimensions, such as insufficient reliability,
robustness, fairness or data protection. It is well-known that trade-offs between these …

Much like cars, AI technologies must undergo rigorous testing to ensure their safety and
reliability. However, just as a 16-wheel truck's brakes are different from that of a standard …

With the recent advances in 5G and 6G communications and the increasing need for
immersive interactions due to pandemic, new use cases such as All-Senses meeting are …

Threat modeling has been successfully applied to model technical threats within information
systems. However, a lack of methods focusing on non-technical assets and their …