A Review on Network Intrusion detection systems based on Machine Learning, Deep Learning and Blockchain for IoT-based healthcare systems
DOI:
https://doi.org/10.21015/vtse.v13i4.2233Abstract
The fast development of the Internet of Things (IoT) in the healthcare sector has raised the issues of data privacy, security, and system integrity. This review deals with a narrative-comparative study of machine learning and deep learning-driven intrusion detection systems (IDS), blockchain-based security architectures, and cryptographic methods to protect IoT-based healthcare settings. The review summarizes the recent studies with the help of synthesis of the findings and mentions the strengths and limitations of each method: While ML/DL-based IDS offer high detection accuracy, they suffer from high false-positive rates and by computational resources; but scalability and latency remain significant challenges and cryptographic methods ensure confidentiality but have to be optimized to address the limited resources of IoT devices. The review has identified substantial research gaps, including the need to have lightweight real-time security models, standardized healthcare specific IoT databank, and enhanced integration of hybrid security frameworks. This work adds the synthesized comparison of methods available to date and presents the perspectives of the research in the future to create an efficient and scalable and multi-layered security solution of IoT-based healthcare systems.
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