A Backbone-Conditioned Pareto Analysis Framework for Preference-Aware IDS Defense Selection

Adversarial robustness is an important consideration for the deployment of artificial intelligence-based intrusion detection systems (IDSs). Existing studies have developed various defense strategies and commonly evaluate them using individual performance measures. However, selecting an appropriate defense strategy remains challenging because different strategies may exhibit distinct advantages under different evaluation objectives and deployment requirements. This study formulates adversarial IDS defense selection as a multi-objective decision problem and proposes a backbone-conditioned preference-aware Pareto analysis framework. The proposed framework evaluates candidate defense strategies from four dimensions. Security threshold filtering is first applied to remove strategies that fail to satisfy minimum robustness requirements, followed by Pareto analysis to identify non-dominated candidates. A preference-aware selection mechanism is then employed to determine the most suitable defense strategy according to different deployment priorities. Experiments on the UNSW-NB15 dataset with multiple IDS backbones demonstrate that the proposed framework can effectively reveal the trade-offs among defense strategies. The results further show that defense selection is influenced by both deployment preferences and backbone architectures, indicating that no single defense strategy is universally optimal across different scenarios. These findings highlight the importance of multi-objective analysis and preference-aware decision-making for adversarial IDS defense strategy selection.