Related papers: An Adversarial Risk Analysis Framework for Cyberse…
In today's evolving threat landscape, ensuring digital sovereignty has become mandatory for military organizations, especially given their increased development and investment in AI-driven cyber security solutions. To this end, a…
Enterprises today face increasing cybersecurity threats that necessitate robust digital forensics and risk mitigation strategies. This paper explores these challenges through an imaginary case study of an organization, a global identity…
This paper presents a novel, structured decision support framework that systematically aligns diverse artificial intelligence (AI) agent architectures, reactive, cognitive, hybrid, and learning, with the comprehensive National Institute of…
In this paper we present a study on using novel data types to perform cyber risk quantification by estimating the likelihood of a data breach. We demonstrate that it is feasible to build a highly accurate cyber risk assessment model using…
Municipalities are vulnerable to cyberattacks with devastating consequences, but they lack key information to evaluate their own risk and compare their security posture to peers. Using data from 83 municipalities collected via a…
This paper presents a multidisciplinary approach to analyzing data from Telegram for early warning information regarding cyber threats. With the proliferation of hacktivist groups utilizing Telegram to disseminate information regarding…
Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) have emerged as promising methodologies for addressing challenges in automated cyber defence (ACD). These techniques offer adaptive decision-making capabilities in…
Based on classical contagion models we introduce an artificial cyber lab: the digital twin of a complex cyber system in which possible cyber resilience measures may be implemented and tested. Using the lab, in numerical case studies, we…
With the increasing concern for security in the network, many approaches are laid out that try to protect the network from unauthorised access. New methods have been adopted in order to find the potential discrepancies that may damage the…
Critical infrastructure systems, including energy grids, healthcare facilities, transportation networks, and water distribution systems, are pivotal to societal stability and economic resilience. However, the increasing interconnectivity of…
Deep learning (DL) has significantly transformed cybersecurity, enabling advancements in malware detection, botnet identification, intrusion detection, user authentication, and encrypted traffic analysis. However, the rise of adversarial…
We are releasing a new suite of security benchmarks for LLMs, CYBERSECEVAL 3, to continue the conversation on empirically measuring LLM cybersecurity risks and capabilities. CYBERSECEVAL 3 assesses 8 different risks across two broad…
With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined…
We present a dependency model tailored to the context of current challenges in data strategies and make recommendations for the cybersecurity community. The model can be used for cyber risk estimation and assessment and generic risk impact…
Regulation, legal liabilities, and societal concerns challenge the adoption of AI in safety and security-critical applications. One of the key concerns is that adversaries can cause harm by manipulating model predictions without being…
Outsourcing of information and communication technologies (ICT) and related services is an established and growing industry. Recent trends, such as the move toward multi-sourcing have increased the complexity and risk of these outsourcing…
Risk is the best known and perhaps the best studied example within a much broader class of cyber security metrics. However, risk is not the only possible cyber security metric. Other metrics such as resilience can exist and could be…
The impact of frontier AI (i.e., AI agents and foundation models) in cybersecurity is rapidly increasing. In this paper, we comprehensively analyze this trend through multiple aspects: quantitative benchmarks, qualitative literature review,…
We provide a comprehensive overview of adversarial machine learning focusing on two application domains, i.e., cybersecurity and computer vision. Research in adversarial machine learning addresses a significant threat to the wide…
Privacy and data protection have become more and more important in recent years since an increasing number of enterprises and startups are harvesting personal data as a part of their business model. One central requirement of the GDPR is…