Preprocessing Network Traffic using Topological Data Analysis for Data Poisoning Detection

The rise of cyber attacks has prompted researchers to develop innovative techniques for detecting malicious activities to improve network security. Data poisoning attacks present a unique challenge when training machine learning (ML) models for the detection of malicious activity within network traffic. Traditional techniques for identifying such data poisoning attacks often lack efficiency when applied to network traffic. In this paper, we propose a novel approach that combines Topological Data Analysis (TDA) with unsupervised learning for preprocessing network traffic, aiming to improve data poisoning detection. TDA enables the capture of complex topological properties and underlying patterns in data sets, which we hypothesize can aid in identifying subtle adversarial modifications within network data. By leveraging TDA combined with an unsupervised learning algorithm, our proposed method can effectively detect poisoned data, enabling developers to remove it before training a MLbased model for network intrusion detection. This work opens up new avenues for research in network security and highlights the potential of TDA for pre-processing network traffic data.