[论文解读] Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks

Shuzhan Wang, Ruxue Jiang+2 more|arXiv (Cornell University)|Jul 8, 2024

Network Security and Intrusion Detection被引用 7

一句话总结

本文提出一种融合模型，将 Isolation Forest、GAN 和 Transformer 结合起来，以提升计算机网络中的异常检测和日志分析的准确性，并减少误报。

ABSTRACT

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.

研究动机与目标

• 解决异常检测和日志分析中高维数据和复杂网络拓扑结构带来的挑战。
• 开发一个统一的融合模型，利用快速异常评分、数据增强和时间序列建模。
• 在网络安全任务中提高鲁棒性和准确性，同时降低误报率。

提出的方法

• 使用 Isolation Forest 快速识别异常数据点。
• 使用 GAN 生成与实际数据分布相匹配的合成数据来增强训练。
• 利用 Transformer 对时间序列数据进行建模并提取上下文以用于检测。
• 利用 Isolation Forest、GAN 和 Transformer 之间的协同作用在异常检测和日志分析中提升性能。

实验结果

研究问题

• RQ1 与基线方法相比，融合模型在检测网络异常方面的效果如何？
• RQ2 在此设置中，基于 GAN 的数据增强是否能提升异常检测性能？
• RQ3 基于 Transformer 的时间序列建模在捕捉用于检测和日志分析的上下文信息方面起到怎样的作用？
• RQ4 在保持或提升检测准确率的同时，集成模型是否能够降低误报警率？

主要发现

• 融合模型在异常检测的准确性方面显著提升。
• 该方法降低了异常检测任务中的误报警率。
• 该模型在日志分析方面也表现良好，能够迅速识别异常行为。
• 集成框架有助于提前发现潜在的网络问题，有助于系统稳定性。