Krishnasamy Sundaramoorthy, Alotaibi Mutlaq B, Alehaideb Lolwah I, Abbas Qaisar
Department of Information Technology, Jerusalem College of Engineering (Autonomous) Pallikaranai, Chennai 600100, Tamil Nadu, India.
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Sensors (Basel). 2023 Nov 20;23(22):9294. doi: 10.3390/s23229294.
In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (FS) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks.
在当前的数字时代,无线传感器网络(WSN)和物联网(IoT)正在不断发展,通过创建一个互联环境来改变人类的体验。然而,确保WSN-IoT网络的安全仍然是一个重大障碍,因为现有的安全模型存在诸如训练时间长和分类过程复杂等问题。在本研究中,提出了一种基于强调农田肥力集成深度感知器网络(EFDPN)的强大的网络物理系统,以增强WSN-IoT的安全性。该举措引入了农田肥力特征选择(FS)技术,以减轻识别和分类攻击的计算复杂性。此外,本研究利用深度感知器网络(DPN)分类算法进行准确的入侵分类,取得了令人印象深刻的性能指标。在分类阶段,采用了灰蝶优化(TSO)模型来改进Sigmoid变换函数,从而提高预测精度。本研究展示了一个基于EFDPN的系统的开发,该系统旨在保护WSN-IoT网络。它展示了DPN分类技术与TSO模型相结合如何显著提高分类性能。在本研究中,我们使用了著名的网络攻击数据集来验证其有效性,揭示了它相对于传统入侵检测方法的优越性,特别是在实现更高的F1分数值方面。F3S算法的纳入在这个框架中起着关键作用,它通过消除无关特征,提高了分类器的预测精度,标志着在加强WSN-IoT网络安全方面迈出了重要一步。本研究提出了一种有前途的方法,以增强在不断发展的WSN-IoT网络环境中互联网络物理系统的安全性和弹性。