Multi-level Gaussian mixture modeling for detection of malicious network traffic

Along with the growing network connectivity across the world, there is a substantial increase in malicious network traffic to exploit the vulnerabilities, thus hampering several organizations and end-users. Though signature-based and classification-based machine learning approaches can detect malicious network traffic, they cannot reliably detect unknown attacks. Several issues are yet unsolved using the existing approaches such as imbalanced training data, high false alarm rate, and lack of detection of unknown attacks. To address these issues, in this work, we propose a novel multi-level classification method that can accurately classify the network traffic into several classes and identify the novel attacks. The unsupervised Gaussian mixture modeling approach is used to learn the statistical characteristics of each traffic category, and an adaptive thresholding technique based on the interquartile range is used to identify any outlier. The proposed work is evaluated on the benchmark CICIDS2017 dataset that includes modern network traffic patterns. The results show a significant improvement relative to the state-of-the-art techniques for detecting unknown attacks and classifying multiple network traffic attacks.

Automated summary

This study introduces a novel multi-level classification method that accurately classifies network traffic and identifies new attacks, addressing issues with existing methods through unsupervised Gaussian mixture modeling and adaptive thresholding techniques, showing significant improvements on modern network traffic datasets compared to state-of-the-art techniques in detecting unknown attacks and classifying multiple network traffic attacks.