Erkuden Rios

In the context of cybersecurity digital twin environments, the ability to simulate realistic network traffic is critical for validating and training intrusion detection systems. However, generating synthetic data that accurately reflects the complex, time-dependent nature of network flows remains a significant challenge. This paper presents an AI-based data generation approach designed to generate multivariate temporal network flow data that accurately reflects adversarial scenarios. The proposed method integrates a Long Short-Term Memory (LSTM) architecture trained to capture the temporal dynamics of both normal and attack traffic, ensuring the synthetic data preserves realistic, sequence-aware behavioral patterns. To further enhance data fidelity, a combination of deep learning-based generative models and statistical techniques is employed to synthesize both numerical and categorical features while maintaining the correct proportions and temporal relationships between attack and normal traffic. A key contribution of the framework is its ability to generate high-fidelity synthetic data that supports the simulation of realistic, production-like cybersecurity scenarios. Experimental results demonstrate the effectiveness of the approach in generating data that supports robust machine learning-based detection systems, making it a valuable tool for cybersecurity validation and training in digital twin environments.

There is a growing concern about the dynamic landscape of cyber security threats escalating, and the need for improvement in defence capabilities against emerging sophisticated incidents. In response, this paper presents a solution called the Cyber Incident Simulation System, which enables system security engineers to simulate cyber-physical attacks and incidents without the requirement to affect or disrupt the ongoing business operation of the system. Leveraging graph-based threat modelling and AI-generated incident data, the system empowers professionals to predict the effect of the incident within the system under study. The synthetic data is used by anomaly-based Intrusion Detection Systems (IDSs) and other additional security controls to improve their detection algorithms to enhance their accuracy and effectiveness. The Cyber Incident Simulation System is designed to enhance the cyber security measures through the simulation of various incident scenarios.

The article provides up-to-date information on the latest Measurement and Signature Intelligence (MASINT) technologies to face Chemical, Biological, Radiological, Nuclear and Explosive (CBRNe) threats and their advancements exploitable by Intelligence and security practitioners. The primary emphasis is on CBRNe technologies, given their central role in MASINT. The discussion encompasses cutting-edge developments that exhibit significant potential in enhancing capabilities for the identification and mitigation of CBRNe risks. In addition, the document puts forth a set of recommendations which cover essential improvements and approaches to effectively address various aspects of CBRNe Intelligence. This comprehensive exploration aims to provide readers, including Intelligence and security practitioners, with valuable insights into the evolving landscape of MASINT technologies addressing the unique challenges posed by CBRNe threats.

With increasing interdependencies and evolving threats, maintaining operational continuity in critical systems has become a significant challenge. This paper presents the DYNABIC (Dynamic business continuity of critical infrastructures on top of adaptive multi-level cybersecurity) approach as a comprehensive framework to enhance the resilience of critical infrastructures. The DYNABIC approach provides the resilience enhancement through dynamic adaptation, automated response, collaboration, risk assessment, and continuous improvement. By fostering a proactive and collaborative approach to resilience, the DYNABIC framework empowers critical infrastructure sectors to effectively mitigate disruptions and recover from incidents. The paper explores the key components and architecture of the DYNABIC approach and highlights its potential to strengthen the resilience of critical infrastructures using the concept of Digital Twins in the face of evolving threats and complex operating environments involving cascading effects.