Wireless Sensor Networks (WSNs) are fundamental to Next-generation Wireless systems, facilitating real-time data collection and analysis in diverse fields such as environmental monitoring, building automation, traffic management, and healthcare. However, their decentralized architecture and limited resources make WSNs particularly vulnerable to Denial-of-Service (DoS) attacks, which can severely disrupt network operations. Ensuring the security and reliability of these networks necessitates robust detection mechanisms for such threats. Hence, this study develops a hybrid model to enhance the detection of DoS attacks in WSNs. Utilizing the widely recognized WSN-BFSF dataset, which contains labelled instances of network activity and various types of DoS attacks, we compare multiple detection approaches. After extensive preprocessing, we implement both traditional and hybrid models, achieving an exceptional accuracy rate of 99.998% with the J48 algorithm. The results demonstrate the superiority of the hybrid approach over the literature review by 0.1%, offering significant improvements in the early detection and mitigation of DoS attacks in WSNs.