An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Internet of Things (IoT) & Connected Devices · Depth: Advanced, long

Summary

An improved CNN-LSTM based intrusion detection model is proposed for IoT networks, addressing the escalating security concerns with the rapid proliferation of IoT devices. This model integrates multi-class classification, combines data from multiple sources for enhanced generalization, and incorporates an LSTM layer to capture temporal features in network traffic, which traditional CNNs often miss. Evaluated using network traffic data, the system achieved an accuracy of approximately 97%. The model was implemented in Python using TensorFlow and Keras, trained on a CPU with a batch size of 32 and 10 epochs, utilizing the Adam optimizer and categorical crossentropy loss. While demonstrating strong performance in detecting DDoS and DoS attacks, the model showed comparatively weaker performance for Reconnaissance attacks, likely due to their similarity to benign traffic patterns. The source code is publicly available on GitHub.

Key takeaway

For Machine Learning Engineers developing IoT security solutions, you should prioritize hybrid CNN-LSTM architectures to capture both spatial and temporal attack patterns. This approach, which achieved 97% accuracy in detecting DDoS and DoS, significantly improves upon traditional CNNs by enabling multi-class classification. Consider refining feature engineering for nuanced threats like reconnaissance attacks to enhance overall detection robustness.

Key insights

Hybrid CNN-LSTM models effectively capture both spatial and temporal features for robust IoT intrusion detection.

Principles

• Hybrid architectures outperform single-model approaches.
• Temporal features enhance intrusion detection.
• Multi-class classification improves real-world applicability.

Method

The method involves preprocessing network traffic data, integrating multiple datasets, and training a hybrid CNN-LSTM model for multi-class classification using Adam optimizer and categorical crossentropy loss.

In practice

• Use CNN-LSTM for IoT network security.
• Implement multi-class detection for diverse threats.
• Preprocess data to handle missing values.

Topics

• IoT Security
• Intrusion Detection Systems
• CNN-LSTM
• Deep Learning
• Network Traffic Analysis
• Multi-class Classification

Code references

• mikhlasmscs25seecs-ship-it/IoT-Intrusion-Detection-CNN-LSTM

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.