Comprehensive Evaluation of Hybrid and XAI-Based Feature Selection for Intrusion Detection: A Smart City Perspective

The expanding connectivity within smart cities has dramatically increased the attack surface, posing significant challenges for Intrusion Detection Systems (IDSs). A critical aspect of effective IDSs is the selection of relevant features to accurately identify potential attackers. Traditional feature selection methods, including filter-based (fast but potentially less accurate), wrapper-based (accurate but computationally intensive), and embedded (classifier-dependent) approaches, each present inherent limitations. Recent advancements propose alternative strategies, such as using Explainable Artificial Intelligence (XAI) algorithms to enhance filtering techniques, hybridizing filter and wrapper methods, and combining these strategies to optimize performance. However, a systematic evaluation of these novel feature selection methods within the context of diverse smart city environments remains largely unexplored. This work presents a comprehensive assessment of feature selection techniques for IDSs in multiple Smart City domains, including healthcare and transportation. Our analysis focuses on evaluating the trade-offs between classification performance and feature reduction achieved by hybrid (IWSHAP), metaheuristics (GRASPQ-FS) and XAI-based approaches (SHAP Ranking). The experimental results indicate that XAI-based methods achieve a favorable trade-off between dimensionality reduction and predictive performance, consistently preserving high F1-Scores (often exceeding 90%) while simultaneously reducing the feature set by substantial margins (e.g., over 90%). Although metaheuristic approaches can achieve superior feature reduction, they often require meticulous tuning to prevent performance degradation. This study underscores the potential of XAI-driven feature selection to enhance IDSs' effectiveness within complex Smart City ecosystems.