Irfan Ullah
Underwater Wireless Sensor Networks (UWSNs) face significant security challenges, particularly Sybil attacks that compromise node authentication and data integrity. This research proposes a hybrid Sybil attack detection model that integrates blockchain technology with anomaly-based detection to ensure secure communication among legitimate nodes. Blockchain provides a tamper-resistant ledger for transaction validation, while the anomaly detection mechanism flags suspicious behavior based on communication patterns. The proposed model was simulated in MATLAB and evaluated against key performance metrics—Packet Delivery Ratio (PDR), Throughput, Energy Consumption, and End-to-End Delay. Results show a notable improvement over baseline and existing models, achieving higher PDR and throughput, and reduced delay and energy usage, validating the model’s effectiveness in enhancing UWSN security.
Irfan Ullah “Enhancing Sybil Attack Detection and Prevention in Underwater Wireless Sensor Network Vol. 12 Issue 08 PP. 126-131 August 2025. https:// doi.org/10.5281/zenodo.16741992.
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