Autonomous Self-Rerouting for Multi-Wormhole Mitigation in Wireless Sensor Networks using XGBoost Ensemble Learning

Abstract

The proliferation of Wireless Sensor Networks (WSNs) in mission-critical applications has made them primary targets for sophisticated routing layer threats, specifically multi-point wormhole attacks that compromise data integrity through artificial low-latency tunnels. This project proposes an Autonomous Self-Rerouting for Multi-Wormhole Mitigation in Wireless Sensor Networks using XGBoost Ensemble Learning to transition network security from passive detection to active, autonomous resilience. Initially, the framework ingests real-time telemetry data, including Round Trip Time (RTT) and Hop-Count Symmetry, which is refined using an Adaptive Feature-Aware Noise Suppression (AFNS) Logic to eliminate environmental jitter and synchronization artifacts. The refined data is then processed by an XGBoost-based Ensemble Classifier, which performs high-dimensional feature extraction to isolate the subtle signatures of colluding malicious nodes. To minimize false positives caused by natural network congestion, a Symptom-Aware Trust Engine (DTE) is integrated to evaluate node reliability over multiple transmission cycles. Once a threat is validated, an Autonomous Mitigation Layer is triggered to logically prune malicious edges from the network topology. The system then utilizes a Cost-Aware Dijkstra’s Algorithm to recalculate secure alternative paths in real-time, ensuring zero-downtime communication. Experimental results demonstrate that the proposed integrated approach maintains a Packet Delivery Ratio (PDR) above 95% even during intense attack scenarios. Ultimately, this framework provides a robust, self-healing solution that significantly improves the reliability and longevity of secure WSN infrastructures.

Introduction

Wireless Sensor Networks (WSNs) are widely used in industrial and surveillance systems, but their decentralized structure makes them highly vulnerable to routing-layer attacks, especially multi-point wormhole attacks. These attacks use colluding malicious nodes to create fake low-latency links, misleading network traffic and severely disrupting data delivery. Traditional security systems often fail because they produce high false alarms, struggle with network noise, and only detect attacks without recovering the network afterward.

To overcome these issues, the proposed system introduces an autonomous self-rerouting framework using Machine Learning, specifically an XGBoost-based ensemble model. The system improves detection accuracy by first applying Adaptive Feature-Aware Noise Suppression (AFNS) to clean network data and then using a Symptom-Aware Trust Engine to verify whether detected anomalies are persistent threats or temporary congestion.

Once a wormhole attack is confirmed, the system automatically triggers a cost-aware rerouting mechanism using Dijkstra’s algorithm to find secure alternative paths, ensuring continuous data flow without manual intervention. This makes the network “self-healing” and significantly reduces downtime.

Existing systems rely on fixed thresholds, manual monitoring, and lack historical trust evaluation, making them less reliable in dynamic environments. In contrast, the proposed framework is adaptive, autonomous, and more accurate in distinguishing real attacks from normal network fluctuations.

Research in this area shows that while methods like CNN-LSTM hybrids, Random Forest models, and trust-based routing improve detection, they still suffer from high computational cost, lack of real-time recovery, or poor performance against complex multi-point wormhole attacks.

The proposed architecture is divided into three layers: data collection and noise filtering, intelligent detection using XGBoost and trust scoring, and autonomous rerouting. Testing results show high performance, with over 98% detection accuracy, less than 2% false alarms, and rerouting latency under 350 ms, making it suitable for real-time, mission-critical applications.

Conclusion

The development of the Autonomous Self-Rerouting Framework marks a significant advancement in securing Wireless Sensor Networks against sophisticated routing-layer threats. By integrating XGBoost Ensemble Learning with a Symptom-Aware Trust Engine, the system moves beyond passive detection to a proactive, \"self-healing\" architecture. Preliminary results confirm that the framework can identify complex multi-point wormhole attacks with over 98% accuracy while maintaining an end-to-end response latency of under 350ms. This ensures that critical data transmission remains uninterrupted even in the presence of colluding malicious nodes. Ultimately, the proposed system provides a scalable and energy-efficient solution for mission-critical WSN applications, such as industrial monitoring and smart city infrastructure. By automating the transition from threat detection to Autonomous Path Recovery, the framework eliminates the need for manual network resets and minimizes data loss. Future iterations of this research will focus on enhancing the model\'s resilience against zero-day exploits and optimizing the AFNS-Logic for even more volatile, high-mobility sensor environments, ensuring long-term network integrity and global reliability

References

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Copyright

Copyright © 2026 Gowtham V, Mahendran M, Monish Kumar K, Ms. Ramya G. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.