LLM-BASED MULTI-CLASS ATTACK ANALYSIS AND MITIGATION FRAMEWORK IN IOT/IIOT NETWORKS
Revolutionizing IoT Security: Hybrid AI for Advanced Attack Analysis & Mitigation
This framework combines Machine Learning for robust multi-class attack detection with Large Language Models for sophisticated behavior analysis and tailored mitigation strategies in IoT/IIoT networks. Discover how it enhances security across diverse environments.
Executive Summary: Pioneering AI in IoT Cybersecurity
In the rapidly expanding landscape of IoT/IIoT, traditional security measures often fall short. Our novel hybrid AI framework addresses this by integrating ML for real-time attack detection and LLMs for deep attack analysis and actionable mitigation, validated across real-world datasets.
9.88/10
Avg LLM Score (ChatGPT-03)
99.30%
ML Detection Accuracy (RF)
13+
Attack Types Covered
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Framework Overview
Attack Detection
LLM Reasoning & RAG
Evaluation & Results
Our hybrid framework integrates ML-based attack detection with LLM-based reasoning. This synergy enables not only rapid identification of diverse threats but also provides human-understandable insights into attack behaviors and context-aware mitigation recommendations. It addresses the critical gap where traditional ML/DL models lack explanatory power and actionable advice.
We benchmarked 9 ML/DL models on Edge-IIoTset and CICIoT2023 datasets. Random Forest consistently outperformed others, achieving F1-scores of 0.9253 and 0.8101 respectively, making it the top performer for multi-class attack detection in our framework. This high accuracy is critical for initiating the LLM analysis stage.
Large Language Models (LLMs) like ChatGPT-03 and DeepSeek-R1 are leveraged for attack behavior analysis and mitigation suggestions. Retrieval-Augmented Generation (RAG) enriches LLM prompts with technical attack descriptions and device specifications from a knowledge base, ensuring context-aware and accurate responses, reducing hallucinations, and tailoring mitigations to specific IoT device constraints.
A novel set of scoring metrics and an ensemble of eight judge LLMs (including ChatGPT-40, DeepSeek-V3, Claude 4 Sonnet) along with human experts were used to quantitatively evaluate LLM responses. ChatGPT-03 consistently outperformed DeepSeek-R1, demonstrating superior attack analysis and mitigation suggestion capabilities across 13 attack types, with average scores of 9.88/10 from judge LLMs.
Deep AI-Driven Threat Insights
9.88 Average LLM (ChatGPT-03) Score on Edge-IIoTset (Judged by ensemble LLMs & Human Experts)Hybrid Framework Workflow
Network Traffic Ingestion
→
ML Attack Detection (Random Forest)
→
Attack Label & Device Info
→
RAG (Knowledge Base Enrichment)
→
LLM Prompt Engineering
→
Attack Analysis & Mitigation (ChatGPT-03)
→
LLM & Human Evaluation
LLM Performance Comparison (ChatGPT-03 vs. DeepSeek-R1)
| Feature | ChatGPT-03 (Avg. Score) | DeepSeek-R1 (Avg. Score) |
|---|---|---|
| Attack Analysis & Threat Understanding | 2.8 / 3 | 2.5 / 3 |
| Mitigation Quality & Practicality | 2.9 / 3 | 2.5 / 3 |
| Technical Depth & Security Awareness | 1.9 / 2 | 1.5 / 2 |
| Clarity, Structure & Justification | 1.9 / 2 | 1.8 / 2 |
- ChatGPT-03 demonstrated deeper technical understanding of exploit mechanics.
- Provided more robust and well-structured mitigations tailored to Raspberry Pi 4 Model B.
Case Study: Password Cracking Mitigation on Raspberry Pi
Our framework successfully analyzed a Password Cracking attack on a Raspberry Pi 4 Model B device. The ML model detected the attack, and ChatGPT-03 provided a detailed analysis of HTTP POST requests, identifying brute-force attempts. Recommended mitigations included implementing account lockout policies (fail2ban), enforcing strong password policies, enabling HTTPS, and adding Multi-Factor Authentication (MFA). These suggestions were device-specific and included practical code/configuration snippets, significantly enhancing the security posture of the IoT device. This practical application highlights the framework's ability to translate detection into actionable security enhancements for vulnerable IoT endpoints.
Calculate Your Potential Security ROI
Estimate the cost savings and reclaimed security analyst hours by integrating advanced AI for IoT threat analysis and mitigation.
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Your Strategic Implementation Roadmap
Our phased approach ensures a smooth integration of the hybrid AI framework into your existing IoT/IIoT security operations.
Phase 1: Discovery & Assessment
Evaluate current IoT security posture, identify critical assets, and define integration points for the hybrid AI framework. Data collection and initial ML model training on relevant traffic.
Phase 2: Framework Deployment & Customization
Deploy the ML detection engine and configure RAG knowledge bases with device-specific and attack-specific information. Customize LLM prompt templates for desired analysis depth.
Phase 3: Integration & Testing
Integrate the framework with existing SIEM/SOAR platforms. Conduct extensive testing across diverse attack scenarios to validate detection accuracy, LLM analysis quality, and mitigation effectiveness.
Phase 4: Monitoring & Optimization
Continuous monitoring of system performance, LLM response quality, and mitigation impact. Iterative refinement of ML models and LLM prompts based on real-world feedback and emerging threats.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat