This paper introduces a novel Intrusion Detection System (IDS) for Wireless Ad hoc Networks (WANs) that leverages Graph Neural Networks (GNNs). The authors address the challenges faced by traditional IDS in dynamic environments, such as rapid node mobility and limited resources, by focusing on meticulous feature engineering and normalization. The proposed GNN-based IDS, strategically placed at zone heads, effectively filters malicious packets while considering the resource constraints of wireless devices. Evaluation on the NSL-KDD dataset shows that the GNN-based IDS outperforms other models like Convolutional Neural Networks (CNNs) and Transformers, highlighting its adaptability to dynamic network environments. The GNN-based IDS excels in understanding complex dependencies, contributing to efficient intrusion detection and network security in resource-constrained WANs. The work emphasizes the transformative role of GNNs in enhancing WAN security and offers innovative solutions against emerging threats. Key contributions include the introduction of GNNs as a core component for resource-efficient IDS, the focus on resource efficiency, and the exploration of various deep learning architectures.This paper introduces a novel Intrusion Detection System (IDS) for Wireless Ad hoc Networks (WANs) that leverages Graph Neural Networks (GNNs). The authors address the challenges faced by traditional IDS in dynamic environments, such as rapid node mobility and limited resources, by focusing on meticulous feature engineering and normalization. The proposed GNN-based IDS, strategically placed at zone heads, effectively filters malicious packets while considering the resource constraints of wireless devices. Evaluation on the NSL-KDD dataset shows that the GNN-based IDS outperforms other models like Convolutional Neural Networks (CNNs) and Transformers, highlighting its adaptability to dynamic network environments. The GNN-based IDS excels in understanding complex dependencies, contributing to efficient intrusion detection and network security in resource-constrained WANs. The work emphasizes the transformative role of GNNs in enhancing WAN security and offers innovative solutions against emerging threats. Key contributions include the introduction of GNNs as a core component for resource-efficient IDS, the focus on resource efficiency, and the exploration of various deep learning architectures.