Related papers: Offensive tool determination strategy R.I.D.D.L.E.…

A common problem in risk analysis is to characterize the overall security of a system of valuable assets (e.g., government buildings or communication hubs), and to suggest measures to mitigate any hazards or security threats. Currently,…

Cloud systems are dynamic environments which make it difficult to keep track of security risks that resources are exposed to. Traditionally, risk assessment is conducted for individual assets to evaluate existing threats; their results,…

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…

ICS environments are vital to the operation of critical infrastructure such as power grids, water treatment facilities, and manufacturing plants. However, these systems are vulnerable to cyber attacks due to their reliance on interconnected…

As AI advances, broader consensus is needed to determine research priorities. This endeavor discusses offensive AI and provides guidance by leveraging Sustainable Development Goals (SDGs) and interpretability techniques. The objective is to…

The internet landscape is growing and at the same time becoming more heterogeneous. Services are performed via computers and networks, critical data is stored digitally. This enables freedom for the user, and flexibility for operators. Data…

Machine learning (ML) models serve as powerful tools for threat detection and mitigation; however, they also introduce potential new risks. Adversarial input can exploit these models through standard interfaces, thus creating new attack…

With AI-based software becoming widely available, the risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. An AI used offensively to attack non-AI assets is referred to…

Cybersecurity threat and risk analysis (RA) approaches are used to identify and mitigate security risks early-on in the software development life-cycle. Existing approaches automate only parts of the analysis procedure, leaving key…

In recent cyber attacks, credential theft has emerged as one of the primary vectors of gaining entry into the system. Once attacker(s) have a foothold in the system, they use various techniques including token manipulation to elevate the…

This study evaluates the application of predictive analytics for real-time cyber-attack detection and response, focusing on how statistical and machine learning methods can improve decision-making in Security Operations Centers (SOCs).…

This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the intervention decision, but their inclusion in decision making is…

As technology advances towards more connected and digital environments, medical devices are becoming increasingly connected to hospital networks and to the Internet, which exposes them, and thus the patients using them, to new cybersecurity…

In the dynamic cyber threat landscape, effective decision-making under uncertainty is crucial for maintaining robust information security. This paper introduces the Cyber Resilience Index (CRI), a threat-informed probabilistic approach to…

In this article, we address the problem of risk assessment of stealthy attacks on uncertain control systems. Considering data injection attacks that aim at maximizing impact while remaining undetected, we use the recently proposed…

The convergence of information and communication technologies has introduced new and advanced capabilities to Industrial Control Systems. However, concurrently, it has heightened their vulnerability to cyber attacks. Consequently, the…

Critical infrastructure increasingly relies on interconnected cyber-physical systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support real-time…

The purpose of the research presented in this article is to develop a dynamic system for forecasting and minimizing the risks of an industrial company based on their quantitative assessment. The article considers the conceptual apparatus of…

Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them require the output probability vector. These attacks can be…

Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack. Some work tries to design…