Evidential classification for defending against adversarial attacks on network traffic

Evidential classification for defending against adversarial attacks on network traffic

Research interest in demonstrating vulnerability of Machine Learning (ML) algorithms against sophisticated Adversarial Machine Learning (AML) perturbation attacks has become more prominent in recent years. Adversarial attacks perturb dataset instances by finding the nearest decision boundary and mov...

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Bibliographic Details
Main Authors: Matt Beechey, Sangarapillai Lambotharan, Kostas Kyriakopoulos
Format: Default Article
Published: 2022
Subjects:
Information and computing sciences
Dempster-Shafer Theory
Evidence theory
Evidential classification
Neural Networks
Adversarial machine learning
Network security
Online Access:https://hdl.handle.net/2134/22560193.v1
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https://hdl.handle.net/2134/22560193.v1

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