Enhanced DOS Attack Detection System in WSNs using Hybrid Model

Wireless Sensor Networks (WSNs) are fundamental to Next-generation Wire-less systems, facilitating real-time data collection and analysis in diverse fields such as environmental monitoring, building automation, traffic man-agement, and healthcare. However, their decentralized architecture and lim-ited 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 mech-anisms for such threats. Hence, this study develops a hybrid model to en-hance 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 approach-es. After extensive preprocessing, we implement both traditional and hybrid models, achieving an exceptional accuracy rate of 99.998\% with the J48 al-gorithm. 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.