相关论文: BEACON: A Unified Behavioral-Tactical Framework fo…
We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured, 150-control behavioral governance layer, the MDBC (Madan DBC) system, applied at inference time to…
Advanced Persistent Threats (APTs) bring significant challenges to cybersecurity due to their sophisticated and stealthy nature. Traditional cybersecurity measures fail to defend against APTs. Cognitive vulnerabilities can significantly…
Modeling and simulation are widely used in cybersecurity research to assess cyber threats, evaluate defense mechanisms, and analyze vulnerabilities. However, the diversity of application areas, the variety of cyberattacks scenarios, and the…
I discuss here three important roles where machine intelligence, brain and behaviour studies together may facilitate criminal law. First, predictive modelling using brain and behaviour data may support legal investigations by predicting…
AI is moving from domain-specific autonomy in closed, predictable settings to large-language-model-driven agents that plan and act in open, cross-organizational environments. As a result, the cybersecurity risk landscape is changing in…
As cybercrime becomes one of the most significant threats facing society today, it is of utmost importance to better understand the perpetrators behind such attacks. In this article, we seek to advance research and practitioner…
Online abuse has grown increasingly complex, spanning toxic language, harassment, manipulation, and fraudulent behavior. Traditional machine-learning approaches dependent on static classifiers and labor-intensive labeling struggle to keep…
Cybercrime is a significant challenge to society, but it can be particularly harmful to the individuals who become victims. This chapter engages in a comprehensive and topical analysis of the cybercrimes that target individuals. It also…
The integration of Large Language Models (LLMs) into cybersecurity education for criminal justice professionals is currently hindered by the "statelessness" of reactive chatbots and the risk of hallucinations in high-stakes legal contexts.…
As cyber threats continue to grow in complexity, traditional security mechanisms struggle to keep up. Large language models (LLMs) offer significant potential in cybersecurity due to their advanced capabilities in text processing and…
Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements…
Cybersecurity demands both rapid pattern recognition and deliberative reasoning, yet purely neural or purely symbolic approaches each address only one side of this duality. Neuro-Symbolic (NeSy) AI bridges this gap by integrating learning…
Institutions with limited data and computing resources often outsource model training to third-party providers in a semi-honest setting, assuming adherence to prescribed training protocols with pre-defined learning paradigm (e.g.,…
Modern digital ecosystems feature complex, dynamic interactions among autonomous entities across diverse domains. Traditional models often separate agents and objects, lacking a unified foundation to capture their interactive behaviors.…
Cybercrime is estimated to cost the global economy almost \$10 trillion annually and with businesses and governments reporting an ever-increasing number of successful cyber-attacks there is a growing demand to rethink the strategy towards…
The rigorous safety verification of control systems in critical applications is essential, given their increasing complexity and integration into everyday life. Simulation-based falsification approaches play a pivotal role in the safety…
Existential risk scenarios relating to Generative Artificial Intelligence often involve advanced systems or agentic models breaking loose and using hacking tools to gain control over critical infrastructure. In this paper, we argue that the…
Large language models are increasingly used as computational tools for modeling human-like behavior. We introduce a behavioral induction framework that modifies model policies through fine-tuning on structured decision-making tasks: using…
The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, particularly focusing on the utilization of Large Language…
Large language models and LLM-based agents are increasingly used for cybersecurity tasks that are inherently dual-use. Existing approaches to refusal, spanning academic policy frameworks and commercially deployed systems, often rely on…