@Article{csse.2025.062413,
AUTHOR = {Alaa Mahmood, İsa Avcı},
TITLE = {Evaluation and Benchmarking of Cybersecurity DDoS Attacks Detection Models through the Integration of FWZIC and MABAC Methods},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {49},
YEAR = {2025},
NUMBER = {1},
PAGES = {401--417},
URL = {http://www.techscience.com/csse/v49n1/60715},
ISSN = {},
ABSTRACT = {A Distributed Denial-of-Service (DDoS) attack poses a significant challenge in the digital age, disrupting online services with operational and financial consequences. Detecting such attacks requires innovative and effective solutions. The primary challenge lies in selecting the best among several DDoS detection models. This study presents a framework that combines several DDoS detection models and Multiple-Criteria Decision-Making (MCDM) techniques to compare and select the most effective models. The framework integrates a decision matrix from training several models on the CiC-DDOS2019 dataset with Fuzzy Weighted Zero Inconsistency Criterion (FWZIC) and Multi-Attribute Boundary Approximation Area Comparison (MABAC) methodologies. FWZIC assigns weights to evaluate criteria, while MABAC compares detection models based on the assessed criteria. The results indicate that the FWZIC approach assigns weights to criteria reliably, with time complexity receiving the highest weight (0.2585) and F1 score receiving the lowest weight (0.14644). Among the models evaluated using the MABAC approach, the Support Vector Machine (SVM) ranked first with a score of 0.0444, making it the most suitable for this work. In contrast, Naive Bayes (NB) ranked lowest with a score of 0.0018. Objective validation and sensitivity analysis proved the reliability of the framework. This study provides a practical approach and insights for cybersecurity practitioners and researchers to evaluate DDoS detection models.},
DOI = {10.32604/csse.2025.062413}
}