# Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2405.18624 · HTML: https://mlsanity.com/paper/2405.18624/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 7 of 7)

- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/1905.05137/) (2019)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review](https://mlsanity.com/paper/2403.13843/) (2024)

## Built on by (most cited first, 9 of 9)

- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models](https://mlsanity.com/paper/2501.13962/) (2025)
- [XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model](https://mlsanity.com/paper/2408.16021/) (2024)
- [Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs](https://mlsanity.com/paper/2507.21193/) (2025)
- [A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security](https://mlsanity.com/paper/2508.12470/) (2025)
- [Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models](https://mlsanity.com/paper/2502.11470/) (2025)
- [Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning](https://mlsanity.com/paper/2504.19030/) (2025)
- [SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments](https://mlsanity.com/paper/2604.06254/) (2026)
- [Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments](https://mlsanity.com/paper/2505.01632/) (2025)

## Abstract (arXiv)

Protecting Internet of things (IoT) devices against cyber attacks is imperative owing to inherent security vulnerabilities. These vulnerabilities can include a spectrum of sophisticated attacks that pose significant damage to both individuals and organizations. Employing robust security measures like intrusion detection systems (IDSs) is essential to solve these problems and protect IoT systems from such attacks. In this context, our proposed IDS model consists on a combination of convolutional neural network (CNN) and long short-term memory (LSTM) deep learning (DL) models. This fusion facilitates the detection and classification of IoT traffic into binary categories, benign and malicious activities by leveraging the spatial feature extraction capabilities of CNN for pattern recognition and the sequential memory retention of LSTM for discerning complex temporal dependencies in achieving enhanced accuracy and efficiency. In assessing the performance of our proposed model, the authors employed the new CICIoT2023 dataset for both training and final testing, while further validating the model's performance through a conclusive testing phase utilizing the CICIDS2017 dataset. Our proposed model achieves an accuracy rate of 98.42%, accompanied by a minimal loss of 0.0275. False positive rate(FPR) is equally important, reaching 9.17% with an F1-score of 98.57%. These results demonstrate the effectiveness of our proposed CNN-LSTM IDS model in fortifying IoT environments against potential cyber threats.