Enhancing Network Security
A Review of Machine Learning Techniques for Detecting TCP SYN Flood Attacks
DOI:
https://doi.org/10.14500/aro.12210Keywords:
Anomaly detection, Distributed denial of service, Deep learning, Machine learning, Network security, Transmission control protocol SYN floodAbstract
Distributed denial of service (DDoS) attacks are a significant danger to network security, with SYN flood assaults being particularly known for exploiting the transmission control protocol (TCP) handshake to deplete server resources. This review paper analyzes the current research on classifying DDoS attacks using machine learning (ML) approaches, with a focus on SYN f lood scenarios. Traditional algorithms such as XGBoost, Random Forest, and k-Nearest Neighbors are examined alongside modern deep learning methods such as convolutional neural networks and long short-term memory networks. Deep learning, noted for its capacity to automatically learn complex properties from data, is particularly effective in dynamic contexts like the internet of things. The review analyzes the usefulness of various strategies, obstacles in feature engineering and model training, and their implications for real-time detection. This study presents a comprehensive overview of the accomplishments in employing ML and deep learning for TCP SYN flood attack classification and exposes gaps in the field that indicate options for further research.
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