## 1 Introduction  
Regardless of the widespread deployment of firewalls and automated attack prevention tools, network intrusions remain a significant concern. Malicious packets and sophisticated cyber-attacks can compromise network integrity, leading to substantial data breatches and operational disruptions. While traditional security measures such as encryption, authentication, and policy management offer foundational protection, they are not foolproof and can be circumvented by advanced persistent threats. Consequently, the implementation of Intrusion Detection Systems (IDSs) has become imperative as a secondary defense mechanism to identify and mitigate unauthorized activities within networks [1; 2]. Recent breakthroughs in machine learning and deep learning have demonstrated potential in improving Intrusion Detection System capabilities within WSNs [3; 4; 5; 6]. [PERSON] et al. suggested a machine learning-based intrusion detection method employing the SMOTE-Tomek Link algorithm to mitigate data imbalance in WSN datasets, resulting in high detection accuracy [1]. [PERSON] et al. performed an extensive survey on the uses of deep reinforcement learning for intrusion detection in IoT environments, emphasizing the efficacy of these methods in dynamic and resource-limited networks [3]. The amalgamation of CNNs with recurrent architectures such as GRUs, in conjunction with attention processes, has been investigated to effectively capture both spatial and temporal characteristics in network traffic data, resulting in enhanced detection efficacy. WSNs represent a class of wireless communication systems composed of compact, resource-constrained sensor nodes that collaborate to collect, transmit, and analyze environmental or physical data [7]. These networks offer several compelling benefits, such as cost efficiency, scalable deployment, low power consumption, and the ability to function without a pre-existing communication infrastructure. Such advantages make WSNs highly suitable for applications in smart cities, environmental monitoring, precision agriculture, and military surveillance [8]. However, WSNs also face inherent challenges due to their physical and operational limitations. These include limited processing power, restricted battery life, narrow bandwidth, short transmission range, and susceptibility to signal interference and hardware noise. These constraints not only impact the reliability of the data being collected and transmitted but also make WSNs vulnerable to external threats, including cyber-attacks, data redundancy, and environmental disruptions. Research has shown that such vulnerabilities significantly reduce the effectiveness and resilience of WSN deployments in real-world environments [7; 8]. Given these challenges, there is a critical need for intelligent and lightweight security mechanisms that can operate effectively within the constraints of WSN architecture. This study addresses this need by proposing a hybrid deep learning-based intrusion detection model capable of detecting a wide range of attacks while maintaining computational efficiency and adaptability across distributed nodes [8]. The primary objective of this study is to develop a lightweight and accurate deep learning-based intrusion detection model tailored for WSNs. Leveraging a hybrid architecture that integrates Conv1D, GRU, and Self-Attention layers, the model is designed to efficiently detect multiple types of network intrusions while addressing the constraints of WSN environments, such as limited energy, computational resources, and data imbalance. This paper presents a hybrid deep learning model that integrates one-dimensional convolutional layers (Conv1 Ds), GRU, and Self-Attention techniques to improve intrusion detection in WSNs. The Conv1D layers are used to collect local spatial characteristics from the input data, whilst the GRU layers capture temporal dependencies, allowing the model to comprehend sequential patterns linked to different sorts of assaults.