RETRACTED: Optimising barrier placement for intrusion detection and prevention in WSNs

PLOS ONE

卷:19

Research Article

C. Kishor Kumar Reddy¹  Vijaya Sindhoori Kaza¹  P. R. Anisha¹  Mousa Mohammed Khubrani²  Mohammed Shuaib²  Shadab Alam²  Sadaf Ahmad³ 

[1] College of Computer Science & IT, Jazan University, Jazan, Saudi Arabia

[2] Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Abids, Hyderabad, Telangana, India

[3] Department of Computer Science, Aligarh Muslim University, Aligarh, India

关键词: Wireless sensor networks;    Optimization;    Algorithms;    Forecasting;    Machine learning algorithms;    Pheromones;    Machine learning;    Unsupervised machine learning;   

DOI  :  10.1371/journal.pone.0299334

 received in 2023-12-31, accepted in 2024-02-07,  发布年份 2024

来源: PLOS

【 摘 要 】

This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method’s effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.

【 授权许可】

CC BY   
Reddy et al 2024

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