Systematic Review of Supervised Learning Models for Network Flood Detection (NFD): Trends, Performance Evaluation, and Implementation Insights

Due to the growing volume, speed, and sophistication of malicious traffic, Network Flood Detection (NFD), especially in the context of Distributed Denial of Service (DDoS) assaults, continues to be a crucial challenge in contemporary network security.  Supervised machine learning has been widely used to enhance the precision, scalability, and real-time detection capabilities of NFD systems.  However, current research reveals inconsistent results on the optimal supervised learning algorithm, mostly because of differences in datasets, feature engineering methods, assessment criteria, and deployment settings.  In order to assess supervised learning models applied to NFD, this study intends to do a Systematic Literature Review (SLR) utilizing the PRISMA framework. A structured search was performed via Scopus, IEEE Xplore, SpringerLink, and ScienceDirect, encompassing papers from 2019 to 2025.  40 primary papers and 16 additional articles were found to be appropriate for synthesis after an initial dataset of 516 research was reviewed using predetermined inclusion and exclusion criteria.  Algorithms, datasets, evaluation criteria, feature selection techniques, and deployment characteristics were all incorporated in the data extraction process. According to the review, models like Random Forest, XGBoost, K-Nearest Neighbor, and Support Vector Machine regularly perform well, with accuracy ranging from 92% to 99%, depending on preprocessing methods and dataset features.  Common problems highlighted include dataset imbalance, lack of real-time adaptation, and insufficient generalization to unforeseen assault types. The results show that supervised learning is still a promising method for NFD, particularly when combined with balanced datasets, hybrid or ensemble model techniques, and optimized feature engineering.  To increase real-time resilience against changing network threats, further research is urged to incorporate deep learning, lightweight edge models, and adaptive learning frameworks.