Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially concerning. Nonetheless, an example generated for a domain with tabular data must be realistic wi...

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Bibliographic Details
Main Authors: João Vitorino, Nuno Oliveira, Isabel Praça
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Future Internet
Subjects:
realistic adversarial examples
adversarial attacks
adversarial robustness
machine learning
tabular data
intrusion detection
Online Access:https://www.mdpi.com/1999-5903/14/4/108

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https://www.mdpi.com/1999-5903/14/4/108

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