Advances in Time Series Methods for Information Security Risk Analysis and Risk Management

Dear Colleagues,

In an increasingly interconnected digital landscape, organizations face a multitude of threats that compromise the confidentiality, integrity, and availability of information assets. Information security risk analysis and risk management have emerged as critical disciplines within cybersecurity, aiming to systematically identify, assess, and mitigate security risks in a structured and measurable manner. Scientific advances in this area have evolved from qualitative risk assessments to more rigorous, data-driven, and quantitative frameworks for addressing the dynamic and adversarial nature of cyber threats. Among these, time series analysis has emerged as a powerful tool for capturing, modeling, and forecasting security threats over time, enabling proactive and adaptive risk mitigation.

We invite you to advance the scientific and practical understanding of information security risk analysis and risk management through time series methodologies. This Special Issue will showcase innovative models, methodologies, and empirical studies that enhance the identification, quantification, and mitigation of cybersecurity risks, while also exploring human, technical, and organizational dimensions in risk management practices.

This Special Issue encourages original research articles and comprehensive reviews that explore time-dependent aspects of cybersecurity risks and their management. We welcome submissions presenting innovative approaches and practical applications that enhance understanding of and advance effective strategies for addressing critical challenges in safeguarding information systems and mitigating cybersecurity risks.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Time series modeling for cybersecurity risk forecasting and anomaly detection;
• Temporal analysis of attack patterns, threat intelligence, and incident trends;
• Probabilistic and statistical approaches to time-dependent risk assessment;
• Machine learning and deep learning for time series-based risk prediction;
• Real-time risk visualization and communication tools;
• Sector-specific time series analysis (e.g., finance, healthcare, critical infrastructure, IoT);
• Emerging time-dependent threats and their implications for risk strategy.

 I look forward to receiving your contributions.

Dr. Yaman Roumani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• time series analysis
• temporal machine learning
• probabilistic risk modeling
• cybersecurity risk assessment
• threat forecasting
• quantitative risk analysis
• adversarial behavior modeling
• time-dependent anomaly detection
• cyber risk prediction