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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Published on: November 1, 2019

An improved X-means and isolation forest based methodology for network traffic anomaly detection.

Yifan Feng¹, Weihong Cai¹, Haoyu Yue¹

¹College of Engineering, Shantou University, ShanTou, Guangdong, China.

|January 31, 2022

Summary

A new X-iForest algorithm improves network traffic anomaly detection using unlabeled data. This method enhances accuracy by clustering abnormal points and normal cluster centers, outperforming existing unsupervised algorithms.

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Area of Science:

Computer Science
Network Security
Machine Learning

Background:

Anomaly detection in large-scale networks is challenging due to complexity and diverse applications.
Unsupervised learning algorithms are crucial for network anomaly detection, especially with unlabeled data.
The accuracy of prior knowledge about abnormal ratios significantly impacts unsupervised algorithm performance.

Purpose of the Study:

To propose a novel unsupervised anomaly detection algorithm, X-iForest, for network traffic.
To enhance anomaly detection accuracy by leveraging X-means for secondary filtering.
To evaluate the performance of X-iForest against mainstream unsupervised algorithms.

Main Methods:

Developed the X-iForest algorithm combining X-means and Isolation Forest (iForest).
Utilized X-means to cluster Euclidean distances between abnormal points and normal cluster centers for secondary filtering.
Compared X-iForest with seven existing unsupervised algorithms.

Main Results:

X-iForest demonstrated notable advantages over seven mainstream unsupervised algorithms.
The algorithm achieved high accuracy in anomaly detection rates and AUC.
Experimental results confirm X-iForest's effectiveness for large-scale network traffic data.

Conclusions:

X-iForest is a robust and effective algorithm for unsupervised anomaly detection in network traffic.
The proposed method offers significant improvements over existing techniques, particularly for large-scale datasets.
X-iForest provides a valuable tool for enhancing network security and monitoring.