Enhancing Cybersecurity in Healthcare: Machine Learning and Deep Learning Strategies for Intrusion Detection on the Internet of Medical Things

Baohan Mo

2024

Abstract

The Internet of Things is increasingly being used in healthcare, leading to rapid growth on the Internet of Medical Things. This technology helps greatly with monitoring patients and collecting data for treatment. However, this combination of technology also introduces significant security threats, especially the risk of intrusions into the Internet of Medical Things (IoMT) systems. This paper evaluates how machine learning and deep learning can improve intrusion detection systems for IoMT. This paper reviewed the current use of Machine Learning (ML) and Deep Learning (DL) in Intrusion Detection Systems (IDS), focusing on systems that detect unusual activities and their effectiveness within the IoMT. By comparing traditional and newer models, such as the PCA-GWO hybrid model, this study highlighted the importance of designing and improving models to identify security threats. The study finds that while ML and DL offer powerful and efficient solutions for detecting intrusions, they also come with challenges in computational demands, data collection and privacy, and making the models easy to explain. Further research can help improve these areas, including optimal algorithms, legal ways to gather data, and using advanced encryption and federated learning to balance efficiency with privacy. The paper concludes that optimized ML and DL techniques can greatly enhance the security of IoMT, ensuring that critical medical data remains intact and private.

Paper Citation

in Harvard Style

Mo B. (2024). Enhancing Cybersecurity in Healthcare: Machine Learning and Deep Learning Strategies for Intrusion Detection on the Internet of Medical Things. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 301-305. DOI: 10.5220/0012937100004508

in Bibtex Style

@conference{emiti24,
author={Baohan Mo},
title={Enhancing Cybersecurity in Healthcare: Machine Learning and Deep Learning Strategies for Intrusion Detection on the Internet of Medical Things},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={301-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012937100004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Enhancing Cybersecurity in Healthcare: Machine Learning and Deep Learning Strategies for Intrusion Detection on the Internet of Medical Things
SN - 978-989-758-713-9
AU - Mo B.
PY - 2024
SP - 301
EP - 305
DO - 10.5220/0012937100004508
PB - SciTePress