School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Computer Science Department, Umm Al-Qura University, Makkah 24381, Saudi Arabia.
Sensors (Basel). 2021 Dec 26;22(1):140. doi: 10.3390/s22010140.
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.
开发网络安全是非常必要的，已经引起了全球学术界和工业界组织的极大关注。为物联网（IoT）提供可持续计算也是非常必要的。机器学习技术在物联网的网络安全中对于入侵检测和恶意识别起着至关重要的作用。因此，在本研究中，我们利用群体智能（SI）算法的优势，为 IDS 系统开发了新的特征提取和选择方法。我们设计了一种基于传统神经网络（CNN）的特征提取机制。之后，我们提出了一种使用最近开发的 SI 算法——Aquila 优化器（AQU）的替代特征选择（FS）方法。此外，为了评估所开发的 IDS 方法的质量，我们使用了四个著名的公共数据集，即 CIC2017、NSL-KDD、BoT-IoT 和 KDD99。我们还考虑了与其他优化方法的广泛比较，以验证所开发方法的竞争性能。结果表明，所开发的方法在使用不同的评估指标时表现出了很高的性能。
