# Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models

arXiv: https://arxiv.org/abs/2502.11470 · HTML: https://mlsanity.com/paper/2502.11470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 97 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2304.14746/) (2023)
- [HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments](https://mlsanity.com/paper/2111.10635/) (2021)
- [Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data](https://mlsanity.com/paper/2406.00016/) (2024)
- [Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems](https://mlsanity.com/paper/2405.18624/) (2024)
- [A Survey of Malware Detection Using Deep Learning](https://mlsanity.com/paper/2407.19153/) (2024)
- [IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset](https://mlsanity.com/paper/2203.16365/) (2022)
- [CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls](https://mlsanity.com/paper/2405.02548/) (2024)
- [Deep Multi-Task Learning for Malware Image Classification](https://mlsanity.com/paper/2405.05906/) (2024)
- [Efficient Intrusion Detection: Combining chi2 Feature Selection with CNN-BiLSTM on the UNSW-NB15 Dataset](https://mlsanity.com/paper/2407.14945/) (2024)
- [Diagnosing Unknown Attacks in Smart Homes Using Abductive Reasoning](https://mlsanity.com/paper/2412.10738/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Novel Active Learning Approach to Label One Million Unknown Malware Variants](https://mlsanity.com/paper/2507.02959/) (2025)

## Abstract (arXiv)

The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduces a novel approach combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and previously unseen attack patterns. A comprehensive evaluation using simulated and real-world traffic data is conducted, with models optimized via Particle Swarm Optimization (PSO). The system achieves an accuracy of up to 99.99% and Matthews Correlation Coefficient (MCC) values exceeding 99.50%. Experiments on NSL-KDD, UNSW-NB15, and CICIoT2023 confirm the model's strong performance across diverse attack types. These findings suggest that the proposed method enhances IoT security by identifying emerging threats and adapting to evolving attack strategies.