A Hybrid Lightweight System for Early Attack Detection in the IoMT Fog

Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. H...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-12, Vol.21 (24), p.8289
Main Authors: Hameed, Shilan S, Selamat, Ali, Abdul Latiff, Liza, Razak, Shukor A, Krejcar, Ondrej, Fujita, Hamido, Ahmad Sharif, Mohammad Nazir, Omatu, Sigeru
Format: Article
Language:English
Subjects:
Bayes Theorem
Big Data
Cloud computing
Deep learning
Early Diagnosis
Field programmable gate arrays
hybrid attack detection
incremental learning
Internet of Things
Intrusion detection systems
IoMT
IoT
Lightweight
Machine learning
Medical equipment
Medical research
Privacy
Sensors
sensor’s data
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT's big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21248289