# Research on CNN-BiLSTM Network Traffic Anomaly Detection Model Based on MindSpore

arXiv: https://arxiv.org/abs/2504.21008 · HTML: https://mlsanity.com/paper/2504.21008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic](https://mlsanity.com/paper/1805.03735/) (2018)
- [Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model](https://mlsanity.com/paper/2105.12096/) (2021)
- [Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows](https://mlsanity.com/paper/2403.10550/) (2024)
- [A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data](https://mlsanity.com/paper/2501.15365/) (2025)
- [Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on the CICIoMT2024 Dataset](https://mlsanity.com/paper/2502.11854/) (2025)

## Abstract (arXiv)

With the widespread adoption of the Internet of Things (IoT) and Industrial IoT (IIoT) technologies, network architectures have become increasingly complex, and the volume of traffic has grown substantially. This evolution poses significant challenges to traditional security mechanisms, particularly in detecting high-frequency, diverse, and highly covert network attacks. To address these challenges, this study proposes a novel network traffic anomaly detection model that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network, implemented on the MindSpore framework. Comprehensive experiments were conducted using the NF-BoT-IoT dataset. The results demonstrate that the proposed model achieves 99% across accuracy, precision, recall, and F1-score, indicating its strong performance and robustness in network intrusion detection tasks.