The term “Internet of Things” (IoT) encompasses an entire group of gadgets that are capable of connecting to the Internet in order to gather and share data. The IoT paradigm is being pushed into computer networks by numerous highly advanced intrusions. Cloud computing greatly enhances the success of the IoT by enabling users to perform computing tasks using Internet-based services accessed through connected devices. This seamless integration of cloud technology and the IoT has become a powerful catalyst, revolutionizing the way we operate. The adoption of a distributed architecture, such as cloud computing, exposes the system to potential threats like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. To mitigate these risks, the concept of an intrusion detection system (IDS) has been introduced within the cloud environment. Various machine learning (ML) and deep learning (DL) algorithms have been proposed and implemented to effectively detect and respond to such malicious traffic in the cloud system. For dimension reduction during the training process of those algorithms, multiple independent and hybrid techniques have been proposed. This study presents an efficient ML-based real-time IDS framework with proposed hybrid feature selection techniques. Additionally, in this study, a concise comparative analysis has been conducted using five well-known public datasets. The findings presented in this paper reveal that our proposed IDS achieved a maximum accuracy of 99.98% in identifying malicious traffic.

“物联网”(IoT) 一词涵盖了一整组能够连接到互联网以收集和共享数据的小工具。物联网范式正被众多高度先进的入侵推入计算机网络。云计算使用户能够使用通过连接设备访问的基于互联网的服务来执行计算任务，从而极大地提高了物联网的成功率。云技术和物联网的无缝集成已成为强大的催化剂，彻底改变了我们的运营方式。采用云计算等分布式架构会使系统面临分布式拒绝服务 (DDoS) 和拒绝服务 (DoS) 攻击等潜在威胁。为了减轻这些风险，云环境中引入了入侵检测系统 (IDS) 的概念。人们已经提出并实现了各种机器学习（ML）和深度学习（DL）算法，以有效地检测和响应云系统中的此类恶意流量。为了在这些算法的训练过程中降维，已经提出了多种独立和混合技术。本研究提出了一种高效的基于 ML 的实时 IDS 框架，并提出了混合特征选择技术。此外，在本研究中，使用五个著名的公共数据集进行了简明的比较分析。本文的研究结果表明，我们提出的 IDS 在识别恶意流量方面的最大准确率达到了 99.98%。