Loading [a11y]/accessibility-menu.js
Black-box Adversarial Machine Learning Attack on Network Traffic Classification | IEEE Conference Publication | IEEE Xplore

Black-box Adversarial Machine Learning Attack on Network Traffic Classification


Abstract:

Deep machine learning techniques have shown promising results in network traffic classification, however, the robustness of these techniques under adversarial threats is ...Show More

Abstract:

Deep machine learning techniques have shown promising results in network traffic classification, however, the robustness of these techniques under adversarial threats is still in question. Deep machine learning models are found vulnerable to small carefully crafted adversarial perturbations posing a major question on the performance of deep machine learning techniques. In this paper, we propose a black-box adversarial attack on network traffic classification. The proposed attack successfully evades deep machine learning-based classifiers which highlights the potential security threat of using deep machine learning techniques to realize autonomous networks.
Date of Conference: 24-28 June 2019
Date Added to IEEE Xplore: 22 July 2019
ISBN Information:

ISSN Information:

Conference Location: Tangier, Morocco

Contact IEEE to Subscribe

References

References is not available for this document.