Date of Award
2026
Document Type
Thesis
Degree Name
MS in Science
Department
Business Analytics and Information Systems
First Advisor
Yanni Ping
Abstract
This study aimed to develop an analytics framework for cyberattack risk, severity, and timing prediction using survival-aware sequence modeling. The primary objective was to analyze the temporal dynamics of cyberattacks by jointly considering attack type, severity, risk, and time-to-attack, and to evaluate whether these components can be effectively modeled within a probabilistic framework. The analysis was conducted using a synthetically generated cyberattack dataset designed for cybersecurity research purposes. The dataset included multiple attack categories, such as DDoS, Intrusion, Malware, severity levels, and engineered risk measures. Python was used as the primary programming language for data analysis and model development. The study applied a combination of statistical and machine learning methods. Exploratory data analysis was used to examine distributions and relationships within the data. Random Forest models were implemented for classification and regression tasks, while survival analysis techniques, including Kaplan-Meier estimation, log-rank tests, and Cox Proportional Hazards modeling, were used to investigate temporal patterns. Additionally, a competing risks framework with cumulative incidence functions was employed to jointly model the probability of different attack type-severity-risk combinations over time. The results showed that cyberattack timing differs significantly across attack type, severity, and risk at the group level, while traditional feature-based models were not sufficient to accurately predict individual timing, indicating a probabilistic rather than deterministic process. The competing risks approach provided a more comprehensive framework by enabling the estimation of likely attack profiles within defined time windows, supporting improved threat prioritization, early detection, and resource allocation in cybersecurity.
Recommended Citation
Voronina, Mariia, "ANALYTICS FRAMEWORK FOR CYBERATTACK RISK, SEVERITY, AND TIMING PREDICTION USING SURVIVAL - AWARE SEQUENCE MODELING" (2026). Theses and Dissertations. 1112.
https://scholar.stjohns.edu/theses_dissertations/1112