相关论文: Cyberattack Action-Intent-Framework for Mapping In…

Membership inference attack (MIA) has become one of the most widely used and effective methods for evaluating the privacy risks of machine learning models. These attacks aim to determine whether a specific sample is part of the model's…

Cyber threats affect all kinds of organisations. Risk analysis is an essential methodology for cybersecurity as it allows organisations to deal with the cyber threats potentially affecting them, prioritise the defence of their assets and…

The increasing interaction of industrial control systems (ICSs) with public networks and digital devices introduces new cyber threats to power systems and other critical infrastructure. Recent cyber-physical attacks such as Stuxnet and…

With the ever-changing landscape of cyber threats, identifying their origin has become paramount, surpassing the simple task of attack classification. Cyber threat attribution gives security analysts the insights they need to device…

As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management. But it is also confronted with various security challenges and potential…

Intrusion Detection Systems (IDS) enhanced with Machine Learning (ML) have demonstrated the capacity to efficiently build a prototype of "normal" cyber behaviors in order to detect cyber threats' activity with greater accuracy than…

Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. The procedure of implementing a IDS for Internet of Things (IoT) networks is not…

This chapter will first present a principal-agent game-theoretic model to capture the interactions between one insurer and one user. The insurer is deemed as the principal who does not have incomplete information about user's security…

Cybercrimes are on the rise, in part due to technological advancements, as well as increased avenues of exploitation. Sophisticated threat actors are leveraging on such advancements to execute their malicious intentions. The increase in…

As large language models (LLMs) grow more capable, concerns about their safe deployment have also grown. Although alignment mechanisms have been introduced to deter misuse, they remain vulnerable to carefully designed adversarial prompts.…

Machine-learning-based Intrusion Detection Systems (IDS) have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This…

To date, formal models of collective intelligence have lacked a plausible mathematical description of the relationship between local-scale interactions between highly autonomous sub-system components (individuals) and global-scale behavior…

An attack taxonomy offers a consistent and structured classification scheme to systematically understand, identify, and classify cybersecurity threat attributes. However, existing taxonomies only focus on a narrow range of attacks and…

Industry 5.0, which focuses on human and Artificial Intelligence (AI) collaboration for performing different tasks in manufacturing, involves a higher number of robots, Internet of Things (IoTs) devices and interconnections,…

Cybersecurity decision-making increasingly occurs in environments characterized by uncertainty, partial observability, and adversarial manipulation, where heterogeneous signals from multiple sources are often incomplete, ambiguous, or…

In the vast domain of cybersecurity, the transition from reactive defense to offensive has become critical in protecting digital infrastructures. This paper explores the integration of Artificial Intelligence (AI) into offensive…

The emerging trend of ubiquitous and pervasive computing aims at embedding everyday devices such as wristwatches, smart phones, home video systems, autofocus cameras, intelligent vehicles, musical instruments, kitchen appliances etc. with…

Physical adversarial attacks are increasingly studied in settings that resemble deployed surveillance systems rather than isolated image benchmarks. In these settings, person detection, multi-object tracking, visible--infrared sensing, and…

Network Intrusion Detection (NID) systems can benefit from Machine Learning (ML) models to detect complex cyber-attacks. However, to train them with a great amount of high-quality data, it is necessary to perform reliable simulations of…

Motivated by the safety and security issues related to cyber-physical systems with potentially multi-rate, delayed, and nonuniformly sampled measurements, we investigate the attack detection and identification using the lifted system model…