Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3055 2019 2019 Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India Piyush Piyush Department of Computer Science, Indian Institute of Technology, Patna, Orchid- 0000-0001-5959-3136, India Deepak Dasaratha Rao Asst. Professor, Dept. of IT, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, TN, India Yasaswini Mandiga Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India Nasib Singh Gill Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India Preeti Gulia Professor and Head Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru, India Piyush Kumar Pareek The Internet of Things (IoT) has revolutionized our daily lives, impacting everything from healthcare to transportation and even home automation and industrial control systems. However, as the number of connected devices continues to rise, so do the security risks. In this review, we explore the different types of attacks that target various layers of IoT infrastructure. To counter these threats, researchers have proposed using machine learning (ML) and deep learning (DL) techniques for detecting different types of attacks. However, our examination of existing literature reveals that the effectiveness of these techniques can vary greatly depending on factors like the dataset used, the features considered, and the evaluation methods employed. Finally, we delve into the current challenges facing Intrusion Detection Systems (IDS) in their mission to protect IoT environments from evolving threats. 2024 2024 367 382 10.54216/JCIM.140227 https://www.americaspg.com/articleinfo/2/show/3055