AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A resilient hybrid defense mechanism aimed to mitigate the impact of two potent adversarial attacks: Fast Gradient Sign Method (FGSM) and Carlini&Wagner (C&W) attack is developed.
A detailed empirical assessment of targeted adversarial vulnerability and defensive behaviour in a multi-class NIDS setting is presented and the results highlight long-standing, class-specific, robustness gaps and provide insights that could be used to design more robust intrusion detection systems.
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