Despite the many benefits of lifestyles and automation, through the Internet of Things, there are risks involved as most of the devices interconnect because of insecurity, compromised devices, and weak encryption. The protectors of cyber security are Intrusion Detection Systems, which are similar to detecting insurgencies by carefully assessing the systems and network activities for signs of malicious activities, unauthorized entry, or potential penetration. This work investigates the possible ways of preventing IoT networks using IDS designed for IoT. This work discusses limitations to a greater extent, suggesting their lack of adaptability and limited resources, and it does so by carrying out the categorization by different varieties and subtypes of the IDS like collaborative, deep learning, feature selection, etc., and then having a research evaluation to decide the pros and cons. After that, the current trends of the IoT environment, promising directions for enhancing security, and specific guidelines for assessing the performance of IDS are discussed. Conclusively, this study further contributes to the continuous establishment of IoT IDS to ensure a safer and longerlasting connected devices future.