This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets as there was confirmed existence of volatility clusters when high volatility periods are followed by low volatility periods. Sensitivity analysis and measures of Value-at-Risk (VaR) and Expected Shortfall (ES) show the amount of losses investors can expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and possibilities for arbitrage opportunities. Findings could be beneficial for investors and policymakers as well as for scientific purposes as findings give us a better understanding of the behaviour of cryptocurrencies.

This study systematically evaluates the vulnerabilities of modern web browsers using developed indices derived from the CVE database, including I_CVE, I_CVSS, I_R and I_T. These indices incorporate metrics such as vulnerability severity and risks, along with browser popularity, to enable a balanced comparison of browser security. The results highlight significant differences in browser security: while Google Chrome and Samsung Internet exhibited lower threat indices, Mozilla Firefox demonstrated consistently higher scores, indicating greater exposure to risks. These observations a slightly contradict widespread opinion. The findings emphasize the importance of timely software updates in mitigating vulnerabilities, as many incidents were linked to outdated browser versions. This study also introduces a robust methodology for assessing browser threats, providing a framework for future research. Potential applications include developing browser-based penetration testing systems to simulate phishing and data extraction scenarios, offering insights into user-specific risks and broader organizational impacts. By combining theoretical analysis with practical implications, this work contributes to advancing browser security and lays the foundation for future applied research in cybersecurity.

This paper presents a standard-oriented architecture for automating information security risk management (ISRM) using artificial intelligence. The study first evaluates eight international frameworks (including COBIT 2019, NIST SP 800-53, and ISO 31000) for automation suitability, identifying ISO/IEC 27005 as the optimal structural foundation. Based on these findings, an architecture integrating Natural Language Processing and machine learning to automate risk identification, assessment, and treatment is proposed. A core component is a decision-making module that combines expert reasoning with a Multi-LLM consensus mechanism to ensure reliability. To provide exploratory support for the proposed architecture, a comparative study using five state-of-the-art Large Language Models (ChatGPT, Gemini Advanced, Grok, Microsoft Copilot, and DeepSeek Chat) was conducted on a standardized risk identification task. The results highlight strong cross-model consensus patterns, providing exploratory evidence that LLMs may support expert-informed risk identification and reasoning tasks while acknowledging the current limitations in complex reasoning. This approach proposes a transparent architectural foundation for AI-driven ISRM whose scalability must be established through future prototype-based evaluation, thereby bridging the gap between rigid compliance standards and generative AI capabilities.

AI-driven cyber threats are evolving faster than current defense mechanisms, complicating forensic investigations. As attacks grow more sophisticated, forensic methods struggle to analyze vast wearable device data, highlighting the need for an advanced framework to improve threat detection and responses. This paper presents a generative artificial intelligence (GenAI)-assisted framework that enhances cyberforensics and strengthens strategic cyber resilience, particularly for chief information security officers (CISOs). It addresses three key challenges: inefficient incident reconstruction, open-source intelligence (OSINT) limitations, and real-time decision-making difficulties. The framework integrates GenAI to automate routine tasks, the cross-layering of digital attributes from wearable devices and open-source intelligence (OSINT) to provide a comprehensive understanding of malicious incidents. By synthesizing digital attributes and applying the 5W approach, the framework facilitates accurate incident reconstruction, enabling CISOs to respond to threats with improved precision. The proposed framework is validated through experimental testing involving publicly available wearable device datasets (e.g., GPS data, pairing and activity logs). The results show that GenAI enhances incident detection and reconstruction, increasing the accuracy and speed of CISOs’ responses to threats. The experimental evaluation demonstrates that our framework improves cyberforensics efficiency by streamlining the integration of digital attributes, reducing the incident reconstruction time and enhancing decision-making precision. The framework enhances cybersecurity resilience in critical infrastructures, although challenges remain regarding data privacy, accuracy and scalability.