EdgeShield: a robust and agile cybersecurity architecture
for the internet of medical things
Dublin Core
Title
EdgeShield: a robust and agile cybersecurity architecture
for the internet of medical things
for the internet of medical things
Subject
Data processing
Dimensionality reduction
Feature selection
Federated learning
Internet of medical things security
Intrusion detection systems
Principal component analysis
Dimensionality reduction
Feature selection
Federated learning
Internet of medical things security
Intrusion detection systems
Principal component analysis
Description
We present EdgeShield, a lightweight pipeline that streamlines internet of medical
things (IoMT) traffic analysis by pairing aggressive dimensionality-reduction
with federated model aggregation. It employs systematic preprocessing, advanced
feature selection, and robust sampling to reduce computational overhead
while enhancing performance. Through feature engineering techniques
such as principal component analysis (PCA), targeted feature selection, and embedding
methods, EdgeShield reduces dataset dimensionality by 96%, enabling
near real-time detection and prevention of cyber attacks on resource-constrained
edge devices. To harden the IoMT perimeter, EdgeShield trains ten lightweight
edge models in just 54s and merges their parameters into a single global classifier
with negligible extra delay. This method requires no additional training
or predictions, thus accelerating deployment. Additionally, by using a compact
dataset with five top-performing features and PCA with two components,
EdgeShield consistently achieves accuracy levels exceeding 99.2% for individual
edge models and the consolidated global model. With a built-in continuous
improvement loop, EdgeShield dynamically adapts to emerging data patterns
and operational conditions, driving substantial advancements in IoMT ecosystem
management. This approach delivers both rapid machine learning model
deployment and robust cyber attack detection, illustrating its potential to revolutionize
IoMT security and elevate healthcare data integrity.
things (IoMT) traffic analysis by pairing aggressive dimensionality-reduction
with federated model aggregation. It employs systematic preprocessing, advanced
feature selection, and robust sampling to reduce computational overhead
while enhancing performance. Through feature engineering techniques
such as principal component analysis (PCA), targeted feature selection, and embedding
methods, EdgeShield reduces dataset dimensionality by 96%, enabling
near real-time detection and prevention of cyber attacks on resource-constrained
edge devices. To harden the IoMT perimeter, EdgeShield trains ten lightweight
edge models in just 54s and merges their parameters into a single global classifier
with negligible extra delay. This method requires no additional training
or predictions, thus accelerating deployment. Additionally, by using a compact
dataset with five top-performing features and PCA with two components,
EdgeShield consistently achieves accuracy levels exceeding 99.2% for individual
edge models and the consolidated global model. With a built-in continuous
improvement loop, EdgeShield dynamically adapts to emerging data patterns
and operational conditions, driving substantial advancements in IoMT ecosystem
management. This approach delivers both rapid machine learning model
deployment and robust cyber attack detection, illustrating its potential to revolutionize
IoMT security and elevate healthcare data integrity.
Creator
Anass Misbah1, Anass Sebbar2, Imad Hafidi1
Source
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
May 26, 2025
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Anass Misbah1, Anass Sebbar2, Imad Hafidi1, “EdgeShield: a robust and agile cybersecurity architecture
for the internet of medical things,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/10188.
for the internet of medical things,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/10188.