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：物联网的发展极大地扩展了我们的日常生活，在智慧城市、医疗保健和建筑等领域的实现中发挥着关键作用。物联网等新兴技术旨在提升认知城市的服务质量。尽管物联网应用在智能建筑场景中具有实用价值，但建筑内大量互联设备采用异构网络，增加了潜在物联网攻击的数量，因此带来了真实风险。物联网应用能够收集和传输敏感数据，故需开发新方法以检测被入侵的物联网设备。本文提出一种基于哈里斯鹰优化和随机权重网络的特征选择模型，用于检测由受损物联网设备发起的物联网僵尸网络攻击。分布式机器学习旨在边缘设备上本地训练模型，无需将数据共享至中央服务器。因此，我们分别采用集中式和分布式机器学习模型应用所提方法。两种学习模型在两个基准数据集上针对物联网僵尸网络攻击进行评估，并使用不同评估指标与其他知名分类技术进行比较。实验结果表明，在大多数情况下，所提方法在准确率、精确率、召回率和F值上均有提升。该方法平均F值最高可达99.9%。结果显示，分布式机器学习模型在保持数据本地化的同时，其性能与集中式机器学习具有竞争力。