Real-Time DDOS Attack Detection and Mitigation Framework for Internet of Medical Things Network: A Comprehensive Review
Dr Joshua Tom
, Wilfred Adigwe , Chioma L. Aworonye , Ifeanyi J. Odegwo
DDoS Attack, IoMTs Network, LSTM-Autoencoder, Particle Swarm Optimization, Real-time Network Monitoring, Anomaly Detection, DDoS Detection System
The Internet of Medical Things (IoMT) has revolutionized healthcare through the use of interconnected medical and wearable devices, among other internet technologies, to monitor and analyze patients' health real time data. IoMT has made remote patient monitoring possible and enhanced the quality of healthcare provision. Nonetheless, the heightened use of round-the-clock network connectivity exposes IoMT networks to threats e.g. distributed denial of service (DDoS) attacks. Disruptions caused by DDoS attacks can have severe consequences on IoMT-based hospital services and delivery, including compromised patient safety and hacked hospital systems. This paper presents a survey of the state-of-the-art DDoS attack detection and mitigation solutions in IoMT environments, along with the challenges therein. We propose a conceptual framework based on deep learning using LSTM-Autoencoders to effectively model the sequential and time-dependent network traffic data and efficient reconstruction of normal traffic patterns for DDoS detection respectively. We also propose to deploy Particle Swarm Optimization (PSO) to enable efficient parameter optimization and effective feature selection. Finally, we highlight some potential future research directions toward ensuring that IoMT systems are secure and resilient.
"Real-Time DDOS Attack Detection and Mitigation Framework for Internet of Medical Things Network: A Comprehensive Review", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 4, page no.a428-a443, April-2026, Available :https://ijsdr.org/papers/IJSDR2604058.pdf
Volume 11
Issue 4,
April-2026
Pages : a428-a443
Paper Reg. ID: IJSDR_308241
Published Paper Id: IJSDR2604058
Downloads: 000140
Research Area: Science and Technology
Country: Owo, Ondo, Nigeria
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
