A Hybrid AI/ML Approach for Real-Time Anomaly Detection in IoT Networks

Abstract

This research presents a breakthrough hybrid AI/ML approach for real-time anomaly detection in IoT networks, achieving 99.47% accuracy on the N-BaLoT dataset - the largest IoT security dataset available. Our novel fusion strategy combines LSTM autoencoders with statistical models to detect IoT botnet activities with unprecedented precision, addressing the critical security challenges in modern IoT ecosystems.

Key Results: Accuracy: 99.47%, Precision: 98.92%, Recall: 99.05%, False Positive Rate: 0.53%

Methodology

Hybrid Architecture Design

Our approach integrates multiple AI/ML techniques in a synergistic framework:

• LSTM Autoencoders: For capturing temporal patterns in IoT network traffic
• Statistical Analysis: Z-score and IQR-based outlier detection methods
• Intelligent Fusion Strategy: Novel combination approach that leverages strengths of both techniques
• Real-time Processing: Optimized for low-latency IoT environments

Dataset & Evaluation

N-BaLoT Dataset: The largest publicly available IoT security dataset containing:

• Real IoT device traffic from 62 different IoT devices
• Multiple botnet attack scenarios (Mirai, Bashlite, etc.)
• Over 7.8 million network flows
• Comprehensive feature extraction including flow statistics and behavioral patterns

Innovation Highlights

• Hybrid Fusion Strategy: Novel approach combining deep learning with statistical methods
• Real-time Capability: Optimized for deployment in production IoT environments
• High Accuracy: Achieved state-of-the-art performance on largest IoT security dataset
• Low False Positives: Critical for practical deployment in IoT networks

Research Results

Performance Achievements

Our hybrid approach demonstrated exceptional performance across all evaluation metrics:

• Accuracy: 99.47% - Highest reported accuracy on N-BaLoT dataset
• Precision: 98.92% - Excellent true positive identification
• Recall: 99.05% - Superior anomaly detection capability
• F1-Score: 98.98% - Balanced performance across precision and recall
• False Positive Rate: 0.53% - Critical for practical deployment

Comparative Analysis

Our hybrid approach significantly outperformed individual methods:

• LSTM Autoencoder alone: 96.8% accuracy
• Statistical methods alone: 94.2% accuracy
• Our Hybrid Approach: 99.47% accuracy

Real-world Impact

This research addresses critical IoT security challenges:

• Scalability: Effective on large-scale IoT networks
• Real-time Processing: Low-latency detection suitable for production
• Practical Deployment: Low false positive rate reduces operational overhead
• Comprehensive Coverage: Detects various types of IoT botnet attacks

Code & Resources

Technical Implementation

• Programming Language: Python
• Deep Learning Framework: TensorFlow/Keras
• Data Processing: Pandas, NumPy, Scikit-learn
• Visualization: Matplotlib, Seaborn
• Statistical Analysis: SciPy, custom statistical modules

Research Contributions

• Novel hybrid fusion strategy for IoT anomaly detection
• State-of-the-art performance on largest IoT security dataset
• Comprehensive evaluation methodology
• Production-ready implementation with real-time capabilities

Publication Status

Authors: Pratham Patel, Prof. Jizhou Tong (Gannon University)
Status: Research Report Complete - Manuscript in Preparation
Expected Submission: 2025
Target Venue: IEEE/ACM Conference on IoT Security