Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sensors (Basel). 2023 Apr 18;23(8):4073. doi: 10.3390/s23084073.
An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT. This article presents a Binary Chimp Optimization Algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The presented BCOA-MLID technique intends to effectively discriminate different types of attacks to secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed for the optimal selection of features to improve intrusion detection efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost regulation extreme learning machine classification model with a sine cosine algorithm as a parameter optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a reduced accuracy of 96.83% and 97.20%, respectively.
物联网（IoT）辅助无线传感器网络（WSNs）是一个系统，其中 WSN 节点和 IoT 设备共同协作，以共享、收集和处理数据。这种结合旨在提高数据分析和收集的有效性和效率，从而实现自动化和改进决策。WSN 辅助 IoT 的安全性可以被视为为保护与 IoT 相关的 WSN 而采取的措施。本文提出了一种基于二进制黑猩猩优化算法和机器学习的入侵检测（BCOA-MLID）技术，用于安全的物联网-WSN。所提出的 BCOA-MLID 技术旨在有效地区分不同类型的攻击，以保护物联网-WSN。在所提出的 BCOA-MLID 技术中，首先进行数据归一化。BCOA 旨在为特征的最优选择设计，以提高入侵检测效果。为了在物联网-WSN 中检测入侵，BCOA-MLID 技术采用了特定于类别的成本调节极端学习机分类模型，以及正弦余弦算法作为参数优化方法。BCOA-MLID 技术的实验结果在 Kaggle 入侵数据集上进行了测试，结果展示了 BCOA-MLID 技术的显著成果，最高准确率为 99.36%，而 XGBoost 和 KNN-AOA 模型的准确率分别降低到 96.83%和 97.20%。
