A Novel Approach for Real-Time Server-Based Attack Detection Using Meta-Learning

Abstract

Modern networks are crucial for seamless connectivity but face various threats, including disruptive network attacks, which can result in significant financial and reputational risks. To counter these challenges, AI-based techniques are being explored for network protection, requiring high-quality datasets for training. In this study, we present a novel methodology utilizing a Ubuntu Base Server to simulate a virtual network environment for real-time collection of network attack datasets. By employing Kali Linux as the attacker machine and Wireshark for data capture, we compile the Server-based Network Attack (SNA) dataset, showcasing UDP, SYN, and HTTP flood network attacks. Our primary goal is to provide a publicly accessible, server-focused dataset tailored for network attack research. Additionally, we leverage advanced AI methods for real-time detection of network attacks. Our proposed meta-RF-GNB (MRG) model combines Gaussian Naive Bayes and Random Forest techniques for predictions, achieving an impressive accuracy score of 99.99%. We validate the efficiency of MRG using cross-validation, obtaining a notable mean accuracy of 99.94% with a minimal standard deviation of 0.00002. Furthermore, we conducted a statistical t-test to evaluate the significance of MRG compared to other top-performing models.

Keywords

• Network security
• Wireshark
• machine learning
• network dataset
• intrusion detection