Can LLMs Effectively Provide Game-Theoretic-Based Scenarios for Cybersecurity?

Game theory has long served as a foundational tool in cybersecurity to test, predict, and design strategic interactions between attackers and defenders. The recent advent of Large Language Models LLMs offers new tools and challenges for the security of computer systems; In this work, we investiga...

Toward an Intent-Based and Ontology-Driven Autonomic Security Response in Security Orchestration Automation and Response

Modern Security Orchestration, Automation, and Response SOAR platforms must rapidly adapt to continuously evolving cyber attacks. Intent-Based Networking has emerged as a promising paradigm for cyber attack mitigation through high-level declarative intents, which offer greater flexibility and...

Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats

Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical...

Immutability Does Not Guarantee Trust: a Formal and Logical Refutation

It is frequently claimed in blockchain discourse that immutability guarantees trust. This paper rigorously refutes that assertion. We define immutability as the cryptographic persistence of historical states in an append-only data structure and contrast it with trust, understood as a rational...

CyGym: a Simulation-Based Game-Theoretic Analysis Framework for Cybersecurity

We introduce a novel cybersecurity encounter simulator between a network defender and an attacker designed to facilitate game-theoretic modeling and analysis while maintaining many significant features of real cyber defense. Our simulator, built within the OpenAI Gym framework, incorporates...

Rational Miner Behaviour, Protocol Stability, and Time Preference: an Austrian and Game-Theoretic Analysis of Bitcoin'S Incentive Environment

This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and...

Automatic Selection of Protections to Mitigate Risks against Software Applications

This paper introduces a novel approach for the automated selection of software protections to mitigate MATE risks against critical assets within software applications. We formalize the key elements involved in protection decision-making - including code artifacts, assets, security requirements,...

Information-Theoretic Estimation of the Risk of Privacy Leaks

Recent work\citeLiu2016 has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation metrics. Specifically, we measure the correlation between the...

DinoCompanion: an Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction

Children's emotional development fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appropriate emotional support. We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for...

Foundation of Affective Computing and Interaction

This book provides a comprehensive exploration of affective computing and human-computer interaction technologies. It begins with the historical development and basic concepts of human-computer interaction, delving into the technical frameworks and practical applications of emotional computing,...

A Dual-Layer Image Encryption Framework Using Chaotic AES with Dynamic S-Boxes and Steganographic QR Codes

This paper presents a robust image encryption and key distribution framework that integrates an enhanced AES-128 algorithm with chaos theory and advanced steganographic techniques for dual-layer security. The encryption engine features a dynamic ShiftRows operation controlled by a logistic map,...

Offensive Robot Cybersecurity

Offensive Robot Cybersecurity introduces a groundbreaking approach by advocating for offensive security methods empowered by means of automation. It emphasizes the necessity of understanding attackers' tactics and identifying vulnerabilities in advance to develop effective defenses, thereby...

HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions

Large language model LLM watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate by changing the next-token predictions output by an LLM. The updated i.e., watermarked predictions depend on random side...

Co-Evolutionary Dynamics of Attack and Defence in Cybersecurity

In the evolving digital landscape, it is crucial to study the dynamics of cyberattacks and defences. This study uses an Evolutionary Game Theory EGT framework to investigate the evolutionary dynamics of attacks and defences in cyberspace. We develop a two-population asymmetric game between attack...

A Linear Approach to Data Poisoning

We investigate the theoretical foundations of data poisoning attacks in machine learning models. Our analysis reveals that the Hessian with respect to the input serves as a diagnostic tool for detecting poisoning, exhibiting spectral signatures that characterize compromised datasets. We use rando...

Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?

Low rank adaptation LoRA has emerged as a prominent technique for fine-tuning large language models LLMs thanks to its superb efficiency gains over previous methods. While extensive studies have examined the performance and structural properties of LoRA, its behavior upon training-time attacks...

DataSentinel: a Game-Theoretic Detection of Prompt Injection Attacks

LLM-integrated applications and agents are vulnerable to prompt injection attacks, where an attacker injects prompts into their inputs to induce attacker-desired outputs. A detection method aims to determine whether a given input is contaminated by an injected prompt. However, existing detection...

Causality for Cyber-Physical Systems

We present a formal theory for analysing causality in cyber-physical systems. To this end, we extend the theory of actual causality by Halpern and Pearl to cope with the continuous nature of cyber-physical systems. Based on our theory, we develop an analysis technique that is used to uncover the...

A Chaos Driven Metric for Backdoor Attack Detection

The advancement and adoption of Artificial Intelligence AI models across diverse domains have transformed the way we interact with technology. However, it is essential to recognize that while AI models have introduced remarkable advancements, they also present inherent challenges such as their...

Mutual Information Minimization for Side-Channel Attack Resistance Via Optimal Noise Injection

Side-channel attacks SCAs pose a serious threat to system security by extracting secret keys through physical leakages such as power consumption, timing variations, and electromagnetic emissions. Among existing countermeasures, artificial noise injection is recognized as one of the most effective...