# Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks

arXiv: https://arxiv.org/abs/2407.05639 · HTML: https://mlsanity.com/paper/2407.05639/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 108 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 23 of 23)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT](https://mlsanity.com/paper/2104.03466/) (2021)
- [VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding](https://mlsanity.com/paper/2312.08733/) (2023)
- [Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach](https://mlsanity.com/paper/2007.09712/) (2020)
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- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review](https://mlsanity.com/paper/2010.14946/) (2020)
- [Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model](https://mlsanity.com/paper/2406.13987/) (2024)
- [Research on Driver Facial Fatigue Detection Based on Yolov8 Model](https://mlsanity.com/paper/2406.18575/) (2024)
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- [Robustness Verification for Knowledge-Based Logic of Risky Driving Scenes](https://mlsanity.com/paper/2312.16364/) (2023)

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## Abstract (arXiv)

Computer network anomaly detection and log analysis, as an important topic in the field of network security, has been a key task to ensure network security and system reliability. First, existing network anomaly detection and log analysis methods are often challenged by high-dimensional data and complex network topologies, resulting in unstable performance and high false-positive rates. In addition, traditional methods are usually difficult to handle time-series data, which is crucial for anomaly detection and log analysis. Therefore, we need a more efficient and accurate method to cope with these problems. To compensate for the shortcomings of current methods, we propose an innovative fusion model that integrates Isolation Forest, GAN (Generative Adversarial Network), and Transformer with each other, and each of them plays a unique role. Isolation Forest is used to quickly identify anomalous data points, and GAN is used to generate synthetic data with the real data distribution characteristics to augment the training dataset, while the Transformer is used for modeling and context extraction on time series data. The synergy of these three components makes our model more accurate and robust in anomaly detection and log analysis tasks. We validate the effectiveness of this fusion model in an extensive experimental evaluation. Experimental results show that our model significantly improves the accuracy of anomaly detection while reducing the false alarm rate, which helps to detect potential network problems in advance. The model also performs well in the log analysis task and is able to quickly identify anomalous behaviors, which helps to improve the stability of the system. The significance of this study is that it introduces advanced deep learning techniques, which work anomaly detection and log analysis.