Internet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute power, a Network Intrusion Detection System is typically employed to detect and remove malicious packets from entering the network. In the same context, we propose feature clusters in terms of Flow, Message Queuing Telemetry Transport (MQTT) and Transmission Control Protocol (TCP) by using features in UNSW-NB15 data-set. We eliminate problems like over-fitting, curse of dimensionality and imbalance in the data-set. We apply supervised Machine Learning (ML) algorithms, i.e., Random Forest (RF), Support Vector Machine and Artificial Neural Networks on the clusters. Using RF, we, respectively, achieve 98.67% and 97.37% of accuracy in binary and multi-class classification. In clusters based techniques, we achieved 96.96%, 91.4% and 97.54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters. Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.

物联网（IoT）设备连接良好；它们生成和使用数据，这些数据涉及在各种设备之间来回传输数据。就物联网而言，确保数据的安全性是一项严峻的挑战。由于物联网设备本质上是低功耗的，不需要大量的计算能力，因此通常采用网络入侵检测系统来检测和删除恶意数据包，使其免于进入网络。在相同的上下文中，我们通过使用UNSW-NB15数据集中的功能，根据流，消息队列遥测传输（MQTT）和传输控制协议（TCP）提出功能群集。我们消除了诸如过度拟合，维数诅咒和数据集不平衡之类的问题。我们采用监督式机器学习（ML）算法，即随机森林（RF），支持向量机和人工神经网络的集群。使用RF，在二进制和多类分类中，我们分别达到98.67％和97.37％的精度。在基于集群的技术中，通过使用RF on Flow和MQTT功能，TCP功能和两个集群的主要功能，我们实现了96.96％，91.4％和97.54％的分类精度。此外，我们表明，与其他基于监督的其他基于ML的最新技术相比，所提出的特征簇具有更高的准确性，并且所需的训练时间更少。两个群集的TCP功能和主要功能。此外，与其他最新的基于监督的ML的方法相比，我们证明了所提出的特征簇具有更高的准确性，并且所需的训练时间更少。两个群集的TCP功能和主要功能。此外，与其他基于监督的其他基于ML的最新技术相比，我们证明了所提出的特征簇具有更高的准确性，并且所需的训练时间更少。