A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks

TL;DR

This paper introduces a hierarchical intrusion detection system for IoMT networks that effectively detects zero-day attacks using a multi-layer framework with meta-learning, ensuring fast, accurate, and privacy-preserving threat detection.

Contribution

It presents a novel multi-level IoMT IDS framework capable of detecting zero-day attacks and distinguishing known from unknown threats without requiring new datasets.

Findings

Achieves 99.77% accuracy and 97.8% F1-score on CICIoMT2024 dataset.

First layer detects zero-day attacks with high accuracy without new datasets.

Meta-learning approach enhances detection performance.

Abstract

The Internet of Medical Things (IoMT) is driving a healthcare revolution but remains vulnerable to cyberattacks such as denial of service, ransomware, data hijacking, and spoofing. These networks comprise resource constrained, heterogeneous devices (e.g., wearable sensors, smart pills, implantables), making traditional centralized Intrusion Detection Systems (IDSs) unsuitable due to response delays, privacy risks, and added vulnerabilities. Centralized IDSs require all sensors to transmit data to a central server, causing delays or network disruptions in dense environments. Running IDSs locally on IoMT devices is often infeasible due to limited computation, and even lightweight IDS components remain at risk if updated models are delayed leaving them exposed to zero-day attacks that threaten patient health and data security. We propose a multi level IoMT IDS framework capable of detecting zero day attacks and distinguishing between known and unknown threats. The first layer (near Edge) filters traffic at a coarse level (attack or not) using meta-learning or One Class Classification (OCC) with the usfAD algorithm. Subsequent layers (far Edge, Cloud) identify attack type and novelty. Experiments on the CICIoMT2024 dataset show 99.77 percentage accuracy and 97.8 percentage F1-score. The first layer detects zero-day attacks with high accuracy without needing new datasets, ensuring strong applicability in IoMT environments. Additionally, the meta-learning approach achieves high.