Summary of Deep Learning-based Anomaly Detection and Log Analysis For Computer Networks, by Shuzhan Wang and Ruxue Jiang and Zhaoqi Wang and Yan Zhou
Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks
by Shuzhan Wang, Ruxue Jiang, Zhaoqi Wang, Yan Zhou
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative fusion model that integrates Isolation Forest, GAN (Generative Adversarial Network), and Transformer to enhance computer network anomaly detection and log analysis. The existing methods are challenged by high-dimensional data and complex topologies, leading to unstable performance and high false-positive rates. The proposed model leverages the strengths of each component: Isolation Forest for identifying anomalous points, GAN for generating synthetic data with real distribution characteristics, and Transformer for modeling and context extraction on time-series data. Experimental results show that the model significantly improves accuracy while reducing the false alarm rate, making it effective in detecting potential network problems. The fusion model also performs well in log analysis tasks, quickly identifying anomalous behaviors. This study introduces advanced deep learning techniques for anomaly detection and log analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding problems on computer networks before they happen. Right now, it’s hard to find these problems because the data is too complicated and the network is too big. The researchers created a new way of analyzing this data by combining three different methods: Isolation Forest, GAN (a special kind of AI), and Transformer. This combination makes it better at finding problems than existing methods. The new method is tested on real data and shows that it can find problems more accurately than before. This means that computer networks will be safer and more reliable. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Gan » Generative adversarial network » Synthetic data » Time series » Transformer
