Improvement on Network Intrusion Detection Using Matrix Profile
Selin Berk, Çağatay Ateş, Mutlu Koca, Emin Anarım · 2025
In this work, a novel method based on matrix profile and machine learning is proposed for anomaly and attack detection in network traffic. Traditional network security systems are often based on signature–based methods, which are inadequate in detecting unknown attacks. The proposed approach utilizes matrix profile, a powerful tool for time series analysis, to identify anomalies in network traffic and classify these anomalies using machine learning models. In the proposed method, the matrix profile is applied to the selected feature set to highlight anomalies. The proposed information–theoretic features play a crucial role in distinguishing attacks. The results obtained on a publicly available dataset demonstrate that the proposed method achieves high accuracy.