The development of the Internet of Things (IoT) has dramatically expanded our daily lives, playing a pivotal role in the enablement of smart cities, healthcare, and buildings. Emerging technologies, such as IoT, seek to improve the quality of service in cognitive cities. Although IoT applications are helpful in smart building applications, they present a real risk as the large number of interconnected devices in those buildings, using heterogeneous networks, increases the number of potential IoT attacks. IoT applications can collect and transfer sensitive data. Therefore, it is necessary to develop new methods to detect hacked IoT devices. This paper proposes a Feature Selection (FS) model based on Harris Hawks Optimization (HHO) and Random Weight Network (RWN) to detect IoT botnet attacks launched from compromised IoT devices. Distributed Machine Learning (DML) aims to train models locally on edge devices without sharing data to a central server. Therefore, we apply the proposed approach using centralized and distributed ML models. Both learning models are evaluated under two benchmark datasets for IoT botnet attacks and compared with other well-known classification techniques using different evaluation indicators. The experimental results show an improvement in terms of accuracy, precision, recall, and F-measure in most cases. The proposed method achieves an average F-measure up to 99.9\%. The results show that the DML model achieves competitive performance against centralized ML while maintaining the data locally.
翻译:互联网信息(IoT)的开发极大地扩大了我们的日常生活,在智能城市、医疗保健和建筑的扶持能力方面发挥着关键作用。 IoT等新兴技术寻求提高认知城市的服务质量。虽然IoT应用有助于智能建筑应用,但确实存在风险,因为这些建筑中大量相互关联的设备,使用各种网络,增加了潜在的IoT攻击次数。IoT应用可以收集和传输敏感数据。因此,有必要开发新的方法,以发现黑化的 IoT 设备。本文建议采用基于Harris Hawks Optimization (HHHHHHO) 和随机 Weight Net 网络(RWN) 的功能选择模型,以探测从受损的 IoT 设备发起的IoT 博特网攻击,但它们构成了真正的风险。分散机器学习(DML) 的目的是在当地对边端设备进行模型培训,而不向中央服务器共享数据。因此,我们采用拟议的模式,使用中央和分布的ML模型。两种学习模型都根据IOT 机器人攻击的基准数据集进行评估,与其他著名的中央精确性数据分析结果相比较,同时用不同的实验性方法显示F-min的成绩分析结果。