ConTMSLE: A Deep Learning Approach Using a Taylor-Based MSLE Loss Function to Detect Blackhole and Sybil Attacks in WSNs

Blackhole and Sybil attacks represent major security threats to Wireless Sensor Networks (WSNs). Detecting these threats is essential to preserving the integrity of network operations. However, their detection is complicated by the various ways they undermine the system, causing data loss to spoofing identities, deceiving trust mechanisms, and ultimately compromising the network’s performance and reliability. Therefore, an effective model, ConvNeXtV2 with Taylor-based Mean Squared Logarithmic Exponential loss (ConTMSLE), is introduced to detect blackhole and Sybil attacks in WSNs. Initially, a WSN simulation is performed. Subsequently, Cluster Head (CH) selection and routing are executed by the Fractional Snow Leopard Beetle Optimization (FraSLepBO), considering energy, trustworthiness, distance, and selfishness. Then, at the base station blackhole and Sybil attacks are identifed. Network log data is processed using Gaussian function-based normalization, and feature selection is done using the Gini Impurity-based Weighted Random Forest (GIWRF). Finally, blackhole and sybil attack detection is done by the ConTMSLE technique. Moreover, the proposed ConTMSLE attains a better True Positive Rate (TPR), accuracy, True Negative Rate (TNR), precision, and F1-Score of 97.839%, 97.309%, 96.584%, 96.029%, and 96.925%, and FraSLepBO performs a better distance, delay, and energy of 8.454 m, 0.618 s, and 90.825 J.