@Article{M-10292,
AUTHOR = {Bello, Abubakar},
TITLE = {CYBER THREATS PREDICTON USING EXPERIENCE SHARING MODEL AND ENSEMBLE LEARNING ALGORITHM},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {3},
YEAR = {2025},
NUMBER = {1},
ARTICLE-NUMBER = {M-10292},
URL = {https://isrdo.org/journal/SRJSET/currentissue/cyber-threats-predicton-using-experience-sharing-model-and-ensemble-learning-algorithm},
ISSN = {2584-0584},
ABSTRACT = {The increasing complexity of cyber
threats, particularly in critical industries such as oil and gas, necessitates
proactive predictive models for early detection and response. Traditional
frameworks such as the Common Vulnerability Scoring System (CVSS) are reactive,
often addressing vulnerabilities post-incident, thereby exposing organizations
to operational and financial risks. This study proposes a novel hybrid
framework combining an experience-sharing model with ensemble machine learning
algorithms, including bagging and boosting techniques. Using structured
datasets such as VERIS and CAPEC, machine learning classifiers—logistic
regression, k-Nearest Neighbors, and regression trees—were employed and
validated using k-fold cross-validation. The results revealed a 94% prediction
accuracy and a 0.96 AUC-ROC score with bagging ensembles, outperforming
conventional models by 12%. A case study focused on Nigeria’s oil and gas
infrastructure validated the model’s sector-specific applicability. This study
contributes to cybersecurity analytics by demonstrating (1) the efficacy of
ensemble learning, (2) a validated experience-sharing paradigm, and (3) the
development of dynamic cyber-risk metrics suited for modern threats. The
proposed framework offers cost-effective and scalable solutions for proactive
threat mitigation.},
DOI = {}
}