Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments
It addresses cybersecurity threats in IoT for security researchers, but is incremental as it synthesizes existing literature without proposing new methods.
This survey paper tackles the problem of designing robust intrusion detection systems for IoT environments by reviewing the latest machine learning-based approaches, highlighting their high accuracy but lack of production-grade models.
IoT as a domain has grown so much in the last few years that it rivals that of the mobile network environments in terms of data volumes as well as cybersecurity threats. The confidentiality and privacy of data within IoT environments have become very important areas of security research within the last few years. More and more security experts are interested in designing robust IDS systems to protect IoT environments as a supplement to the more traditional security methods. Given that IoT devices are resource-constrained and have a heterogeneous protocol stack, most traditional intrusion detection approaches don't work well within these schematic boundaries. This has led security researchers to innovate at the intersection of Machine Learning and IDS to solve the shortcomings of non-learning based IDS systems in the IoT ecosystem. Despite various ML algorithms already having high accuracy with IoT datasets, we can see a lack of sufficient production grade models. This survey paper details a comprehensive summary of the latest learning-based approaches used in IoT intrusion detection systems, and conducts a thorough critical review of these systems, potential pitfalls in ML pipelines, challenges from an ML perspective, and discusses future research scope and recommendations.