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IoT as a domain has grown so much in the last few years that it rivals thatof the mobile network environments in terms of data volumes as well ascybersecurity threats. The confidentiality and privacy of data within IoTenvironments have become very important areas of security research within thelast few years. More and more security experts are interested in designingrobust IDS systems to protect IoT environments as a supplement to the moretraditional security methods. Given that IoT devices are resource-constrainedand have a heterogeneous protocol stack, most traditional intrusion detectionapproaches don't work well within these schematic boundaries. This has ledsecurity researchers to innovate at the intersection of Machine Learning andIDS to solve the shortcomings of non-learning based IDS systems in the IoTecosystem.  Despite various ML algorithms already having high accuracy with IoT datasets,we can see a lack of sufficient production grade models. This survey paperdetails a comprehensive summary of the latest learning-based approaches used inIoT intrusion detection systems, and conducts a thorough critical review ofthese systems, potential pitfalls in ML pipelines, challenges from an MLperspective, and discusses future research scope and recommendations.

Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments