SHIELD-IDS: Structurally Heterogeneous Ensemble with Integrated Layered Defense for Intrusion Detection Systems
arXiv:2606.07716v1 Announce Type: cross Abstract: Adversarial attacks pose a serious and growing threat to Machine Learning (ML)-based Intrusion Detection Systems (IDS), where imperceptible perturbations to network flow features can systematically mislead classifiers into accepting malicious traffic as benign. The IDS-Anta framework partially addresses this through Z-score normalization, Singular Value Decomposition (SVD), and Multi-Armed Bandit (MAB) classifier selection with Thompson Sampling, yet its classifier pool lacks sufficient structural diversity for robust adversarial resistance. Th
The increasing reliance on Machine Learning for critical infrastructure like intrusion detection systems necessitates advanced defense mechanisms against adversarial attacks, which are themselves evolving rapidly.
Securing AI-based defense systems against sophisticated adversarial attacks is crucial for maintaining integrity and trust in cybersecurity and preventing catastrophic failures or data breaches caused by manipulated systems.
The proposed 'SHIELD-IDS' suggests a more robust and structurally diverse approach to defending AI-based Intrusion Detection Systems, potentially shifting the defensive paradigm in cybersecurity.
- · Cybersecurity firms developing robust AI defenses
- · Organizations relying on ML-based IDS
- · National security agencies
- · Adversarial attackers
- · Providers of less robust IDS solutions
ML-based Intrusion Detection Systems become more resilient to adversarial attacks, reducing vulnerabilities.
Increased trust in AI-driven cybersecurity leads to broader adoption of AI in critical infrastructure defense.
The arms race between AI defense and offense escalates, driving continuous innovation in both fields.
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Read at arXiv cs.LG