Two detectors achieve near-perfect accuracy detecting PANDA-style adversarial attacks on autoencoder NIDS using image-space error localization and packet-feature consistency checks on IoT traffic.
Evasion adversarial attacks remain impractical against ml-based network intrusion detection systems, especially dynamic ones
2 Pith papers cite this work. Polarity classification is still indexing.
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Experiments with around 2200 variations show that shallower networks with reduced features and ReLU activation reduce adversarial vulnerability in ML-NIDS and outperform deeper adversarially trained models while keeping high clean-data performance.
citing papers explorer
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Detecting Adversarial Evasion Attacks Against Autoencoder-Based Network Intrusion Detection Systems
Two detectors achieve near-perfect accuracy detecting PANDA-style adversarial attacks on autoencoder NIDS using image-space error localization and packet-feature consistency checks on IoT traffic.
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A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
Experiments with around 2200 variations show that shallower networks with reduced features and ReLU activation reduce adversarial vulnerability in ML-NIDS and outperform deeper adversarially trained models while keeping high clean-data performance.