WSN Attack Detection Using Attentive Dual Residual Generative Adversarial Networks
Keywords:
Adaptive distorted Gaussian matched filter, Attentive Dual Residual Generative Adversarial Networks, Ladybug Beetle Optimization, Wireless Sensor NetworkAbstract
Wireless sensor networks (WSN) play an important role in different industries because of lack infrastructure but remain vulnerable to numerous attacks. Denial-of-Service (Dos) attacks are the main attack that scares WSN. If a DoS attacks targets a company, it could lower that company's perceived value in the market. To minimize these issues this research article presents an approach of a WSN Attack detection using Attentive Dual Residual Generative Adversarial Networks (WSN-AD-ADRGAN) technique. Initially, input data is collected from WSN-DS. Afterward the data are fed to preprocessing. The pre processing segment removes noise and redundant data by utilizing Adaptive distorted Gaussian matched filter (ADGMF)and the preprocessed output fed to Attentive Dual Residual Generative Adversarial Networks (ADRGAN) method that classifies the WSN attacks into Black hole attack, Grey hole attack, Flooding attack, Timing attack ,normal. Attentive Dual Residual Generative Adversarial Network classifier, in general, does not describe modifying optimization techniques to identify optimum parameters to enable accurate WSN attack classification. Therefore, it is proposed to use Ladybug Beetle Optimization Algorithm (LBOA) to optimize Attentive Dual Residual Generative Adversarial Network, which accurately classifies WSN attacks. The proposed WSN-AD-ADRGAN technique is implemented in python utilizing WSN-DS dataset. The suggested method's performance is examined using performance metrics like precision, recall, accuracy, F1-score, specificity, ROC, computing time. The proposed AIPE-DBO-LDC-CXR method attains higher accuracy 16.65%, 18.85% and 16.45%; greater sensitivity 16.34%, 12.23%, and 19.12%; greater specificity 14.89%, 16.89% and 20.67% and 82.37%, 87.76% and 78.78% lower computational time analyzed to the existing methods like intrusion identification system in WSN utilizing conditional generative adversarial network (IDS-WSN-CGAN),Service attack improvement in wireless sensor network under machine learning (SAI-WSN-ML),Machine learning based identification, EC-BRTT technique based DoS attacks prevention in wireless sensor networks (ML-DDoS-WSN) respectively.
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