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A feature-driven Intrusion Detection (ID) framework that combines XGBoost-based feature selection with multiple ML classifiers to improve attack detection performance while reducing computational complexity is proposed.
Results demonstrate that jointly incorporating structural, semantic, and sentiment information yields more coherent, semantically aligned communities than structure-only baselines while maintaining practical scalability.
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