Martijn Dekker

We describe an approach to detect the frequency of interaction between any two genomic loci. Generation of a matrix of interaction frequencies between sites on the same or different chromosomes reveals their relative spatial disposition and provides information about the physical properties of the chromatin fiber. This methodology can be applied to the spatial organization of entire genomes in organisms from bacteria to human. Using the yeast Saccharomyces cerevisiae, we could confirm known qualitative features of chromosome organization within the nucleus and dynamic changes in that organization during meiosis. We also analyzed yeast chromosome III at the G1 stage of the cell cycle. We found that chromatin is highly flexible throughout. Furthermore, functionally distinct AT- and GC-rich domains were found to exhibit different conformations, and a population-average 3D model of chromosome III could be determined. Chromosome III emerges as a contorted ring.

Cyber threats continue to evolve in complexity, thereby traditional cyber threat intelligence (CTI) methods struggle to keep pace. AI offers a potential solution, automating and enhancing various tasks, from data ingestion to resilience verification. This paper explores the potential of integrating artificial intelligence (AI) into CTI. We provide a blueprint of an AI-enhanced CTI processing pipeline and detail its components and functionalities. The pipeline highlights the collaboration between AI and human expertise, which is necessary to produce timely and high-fidelity cyber threat intelligence. We also explore the automated generation of mitigation recommendations, harnessing AI’s capabilities to provide real-time, contextual, and predictive insights. However, the integration of AI into CTI is not without its challenges. Thereby, we discuss the ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions. We address the need for data privacy, consent mechanisms, and the potential misuse of technology. Moreover, we highlight the importance of addressing biases both during CTI analysis and within AI models, warranting their transparency and interpretability. Lastly, our work points out future research directions, such as the exploration of advanced AI models to augment cyber defenses, and human–AI collaboration optimization. Ultimately, the fusion of AI with CTI appears to hold significant potential in the cybersecurity domain.

Abstract The challenge of decision‐making under uncertainty in information security has become increasingly important, given the unpredictable probabilities and effects of events in the ever‐changing cyber threat landscape. Cyber threat intelligence provides decision‐makers with the necessary information and context to understand and anticipate potential threats, reducing uncertainty, and improving the accuracy of risk analysis. The latter is a principal element of evidence‐based decision‐making, and it is essential to recognize that addressing uncertainty requires a new, threat‐intelligence (TI) driven methodology, and risk analysis approach. We propose a solution to this challenge by introducing a TI‐based security assessment methodology and a decision‐making strategy that considers both known unknowns and unknown unknowns. The proposed methodology aims to enhance the quality of decision‐making by utilizing causal graphs, which offer an alternative to conventional methodologies that rely on attack trees, resulting in a reduction of uncertainty. Furthermore, we consider tactics, techniques, and procedures that are possible, probable, and plausible, improving the predictability of adversary behavior. Our proposed solution provides practical guidance for information security leaders to make informed decisions in uncertain situations. This paper offers a new perspective on addressing the challenge of decision‐making under uncertainty in information security by introducing a methodology that can help decision‐makers navigate the intricacies of the dynamic and continuously evolving landscape of cyber threats.

In 2004, the main contenders in the American presidential election, incumbent Republican president George Bush and Democratic challenger John Kerry, appeared with their wives in two separate episodes of the Dr. Phil show. They talked with America's most popular television therapist about their families and how they combined family life and political career. Campaign and political issues were purposively kept out of the conversations. Analysis of the audience's responses to these two shows, posted on a website, shows that the political relevance of the private and family lives of the candidates was heavily contested. However, the family life and values of the discussants themselves were seen as a legitimate point of departure for their political positions. Thus, the Dr. Phil forum functioned both as a place of deliberation and dialogue, and as a site for articulating political viewpoints.

The challenge of decision-making under uncertainty in information security has become increasingly important, given the unpredictable probabilities and effects of events in the ever-changing cyber threat landscape. Cyber threat intelligence provides decision-makers with the necessary information and context to understand and anticipate potential threats, reducing uncertainty and improving the accuracy of risk analysis. The latter is a principal element of evidence-based decision-making, and it is essential to recognize that addressing uncertainty requires a new, threat-intelligence driven methodology and risk analysis approach. We propose a solution to this challenge by introducing a threat-intelligence based security assessment methodology and a decision-making strategy that considers both known unknowns and unknown unknowns. The proposed methodology aims to enhance the quality of decision-making by utilizing causal graphs, which offer an alternative to conventional methodologies that rely on attack trees, resulting in a reduction of uncertainty. Furthermore, we consider tactics, techniques, and procedures that are possible, probable, and plausible, improving the predictability of adversary behavior. Our proposed solution provides practical guidance for information security leaders to make informed decisions in uncertain situations. This paper offers a new perspective on addressing the challenge of decision-making under uncertainty in information security by introducing a methodology that can help decision-makers navigate the intricacies of the dynamic and continuously evolving landscape of cyber threats.