<![CDATA[Wireless sensor networks (WSNs) have revolutionized data collection in diverse environments, from industrial settings to natural ecosystems. However, their decentralized nature and energy constraints pose unique security and operational challenges. Previous research provided foundational insights into WSN security but lacked comprehensive strategies for real-time intrusion prevention and efficient energy utilization. Our work employs a multi-layered approach, integrating network intrusion prevention systems (NIPS) with WSNs and leveraging machine learning for threat detection. We developed MinE-DT (minimum energy-direct transmission) hybrid routing an integrated WSN model that not only identifies and mitigates distributed denial-of-service (DDoS) attack but also optimizes energy consumption, ensuring prolonged network longevity without compromising security. The proposed model's distinctiveness lies in its fusion of NIPS with energy-saving algorithms, offering a dual advantage of enhanced security and energy efficiency. Utilizing a combination of simulations and theoretical analysis, our methodology yielded promising results, showcasing significant improvements in threat detection rates and energy conservation.]]>
