Research Domain

Explore the foundational research, objectives, and methodologies behind the Real-Time SIEM-Based Cybersecurity Framework for IoMT Environments.

Literature Survey

The rapid growth of the Internet of Medical Things (IoMT) has reshaped healthcare by connecting life-critical devices. While this improves efficiency, it exposes severe cybersecurity risks due to outdated firmware and proprietary communication protocols. Conventional IDS and SIEMs struggle against zero-day medical vulnerabilities and lack healthcare-specific context like automatic PHI redaction and offline capabilities.

The Research Gap

  • High False Positives: Anomaly-based approaches struggle with medical traffic variability.
  • Resource Constraints: Hospital gateways lack GPU acceleration for heavy models.
  • Lack of Explainability: Black-box models generate predictions without reasoning.
  • No Automated Context: Lack of automatic rollback and PHI redaction.
Research Challenge

The Core Problem Statement

"How to design and implement a holistic, automated response and monitoring system for IoMT devices that can accurately distinguish compromised equipment, intelligently correlate alerts, automatically contain threats via network isolation, and redact PHI while operating efficiently on resource-constrained infrastructure?"

The Research Methodology

A multi-phase approach to building a resilient IoMT security framework.

1

Data Ingestion & Pre-processing

Capturing live MQTT, HL7, and DICOM traffic via Suricata sensors and specialized lightweight agents.

2

AI Threat Detection (ML/DL)

Processing data through hybrid ensemble models optimized for edge devices using quantization and pruning.

3

AICE Correlation & Response

The AICE engine clusters alerts to generate severity scores and triggers automated containment scripts.

Methodology Architecture
Research Innovation

Unique Offered Features

The specialized technical capabilities that distinguish our 4-layer IoMT framework.

PROTOCOL: AICE-CORRELATION

Autonomous Incident Correlation

An intelligent engine that fuses heterogeneous alerts from network and host layers to reduce false positives by 40%.

PROTOCOL: PRIVACY-MASK

Dynamic PHI Redaction

Ensuring HIPAA compliance through AI-driven masking of sensitive patient health information in real-time telemetry.

PROTOCOL: ZERO-TRUST-ISO

Proactive Threat Isolation

Autonomous containment protocol that predicts and isolates infected sensor nodes in <30s to prevent lateral movement.

PROTOCOL: SELF-HEAL-V1

Self-Healing Firmware Rollback

Integrated hardware mechanism to automatically restore compromised sensor firmware to a verified "last-known-good" state.

PROTOCOL: LIGHT-CLASSIFY

Resource-Optimized AI

Specially pruned ML models designed to run on resource-constrained ESP32 nodes without compromising detection accuracy.

PROTOCOL: QUAD-SYNC

Full-Stack Layer Synergy

Seamless bidirectional communication between Hardware, AI, Correlation, and Response layers for unified defense.

Hardware & Implementation

From schematic to silicon: Validating the framework on physical medical nodes.

Full System Architecture

System Architecture

Node Hardware Schematic

Hardware Schematic

Monitoring Stack

Wazuh SIEM, ELK Stack, Suricata IDS, Apache Kafka

AI Frameworks

TensorFlow Lite, Scikit-Learn, SHAP, Python 3.10

Edge Infrastructure

ESP32 Nodes, Raspberry Pi 4 Gateways, Docker, MQTT