Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables

James Msughter Adeke, Guangjie Liu (), Junjie Zhao, Nannan Wu and Hafsat Muhammad Bashir
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James Msughter Adeke: School of Electronics & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Guangjie Liu: School of Electronics & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Junjie Zhao: School of Electronics & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Nannan Wu: School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
Hafsat Muhammad Bashir: School of Electronics & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Future Internet, 2023, vol. 15, issue 12, 1-22

Abstract: Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs.

Keywords: machine learning; adversarial attack; network traffic classification; derived variables; robustness (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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