Friday, October 10, 2025

Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest


In a major breakthrough for network security, researchers have proposed a Tabu search-based optimized Random Forest model to strengthen intrusion detection systems (IDS) in Wireless Sensor Networks (WSNs). With the rapid expansion of IoT and sensor-based applications, ensuring data integrity and network reliability has become a top priority.

The study introduces an intelligent optimization approach where the Tabu search algorithm fine-tunes the parameters of the Random Forest classifier, achieving higher accuracy and lower false alarm rates. This hybrid model enhances anomaly detection efficiency, allowing real-time identification of potential security threats within sensor nodes.

Unlike traditional IDS models, the Tabu search-enhanced framework delivers superior performance while maintaining low computational cost and energy consumption — two critical factors in WSN environments. The results show remarkable improvement in detecting malicious behaviors, making it a valuable contribution to secure and energy-efficient IoT systems.

This advancement not only boosts network reliability and resilience but also paves the way for AI-driven cybersecurity solutions in next-generation wireless technologies.

Popular Engineer Awards

Theme: Popular Engineer Awards for a Connected Future

Popular Engineer Awards celebrate groundbreaking contributions in the field of research data analysis. This year’s theme, "Popular Engineer Awards for a Connected Future," highlights the latest innovations, methodologies, and transformative applications that drive scientific discovery and practical solutions.

By recognizing outstanding researchers, teams, and organizations, these awards aim to:

Honor Excellence – Acknowledge remarkable achievements in data-driven research and innovation.

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