New hybrid defense enhances NIDS against adversarial attacks

Researchers have developed a hybrid defense mechanism to protect Network Intrusion Detection Systems (NIDS) from adversarial attacks. This approach combines Adversarial Training (AT) and Gaussian Data Augmentation (GDA) to mitigate the impact of Fast Gradient Sign Method (FGSM) and Carlini & Wagner (C&W) attacks. The proposed method significantly improved NIDS accuracy from a post-attack low of 0.2649 (FGSM) and 0.4961 (C&W) to 96.57% and 89.20%, respectively, demonstrating its effectiveness in enhancing NIDS robustness. AI

IMPACT Enhances the security of AI-powered network intrusion detection systems against sophisticated attacks.

RANK_REASON Academic paper detailing a new defense mechanism for network security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Adversarial Training
• Carlini & Wagner
• Fast Gradient Sign Method
• FGSM
• Gaussian Data Augmentation
• Khushnaseeb Roshan
• Network Intrusion Detection System

• paper
• safety

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