Enhanced WSN‐IoT Security in Cyber‐Physical Systems Employing a Directed Acyclic Graph Triplet Attention Network and Draco Lizard Optimizer

ABSTRACT Wireless Sensor Network‐Internet of Things security in Cyber‐Physical Systems (CPSs) focuses on protecting interconnected sensor networks and devices from cyber threats while ensuring reliable operation of physical processes. However, these systems face challenges such as limited device resources, high vulnerability to attacks, and difficulties in maintaining data integrity and privacy. This research introduces a novel blockchain‐based wireless sensor network and Internet of Things (WSN‐IoT) security framework that makes use of a Directed Acyclic Graph Triplet Attention Network coupled with the Draco Lizard Optimizer (DAGTAN‐DLO) as an optimization technique. It first collects network traffic data from the UNSW‐NB15 and NSL‐KDD datasets, followed by pre‐processing using Adaptive Multiple Imputation of Missing Values with Class Center (AMIMVC), which reduces noise and normalizes features. Then the Empowering Decision Transformer (EDT) is used to extract compact and discriminative features, and the Wonderful Fairy Wren Optimization Algorithm (SFOA) is used to select the most relevant features. Ultimately, these features are classified by Directed Acyclic Graph Triplet Attention Network (DAGTAN) into normal or anomalous traffic, with classification accuracy improved through weight optimization using the Draco Lizard Optimizer (DLO). The encrypted data is then securely stored using Blockchain‐Based Ultra‐Low Storage overhead Practical Byzantine Fault Tolerance (ULS‐PBFT), thus allowing for privacy‐preserving storage with the integration of smart contracts and cloud services. The proposed network traffic monitoring framework not only effectively and accurately detects cyber threats but also manages data securely and efficiently in the WSN‐IoT‐enabled CPS environments. The proposed technology achieves up to 99.89% accuracy, 99.43% F1‐score, and exceptionally high specificity and precision on UNSW‐NB15 and NSL‐KDD datasets, offering a scalable, precise, and blockchain‐secured solution for cyber threat detection in WSN‐IoT‐enabled CPS environments.