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.

翻译：计算机网络异常检测与日志分析作为网络安全领域的重要课题，一直是保障网络安全与系统可靠性的关键任务。现有网络异常检测与日志分析方法往往面临高维数据与复杂网络拓扑的挑战，导致性能不稳定且误报率较高。此外，传统方法通常难以处理对异常检测与日志分析至关重要的时序数据。因此，我们需要更高效、准确的方法来应对这些问题。为弥补现有方法的不足，我们提出一种融合Isolation Forest、GAN（生成对抗网络）与Transformer的创新模型，三者各司其职：Isolation Forest用于快速识别异常数据点，GAN通过生成具有真实数据分布特征的合成数据以扩充训练数据集，而Transformer则用于对时序数据进行建模与上下文提取。三者的协同作用使我们的模型在异常检测与日志分析任务中更具准确性与鲁棒性。我们通过大量实验评估验证了该融合模型的有效性。实验结果表明，我们的模型在显著提升异常检测准确率的同时降低了误报率，有助于提前发现潜在网络问题。该模型在日志分析任务中同样表现优异，能够快速识别异常行为，有助于提升系统稳定性。本研究的价值在于引入了先进的深度学习技术，为异常检测与日志分析工作提供了新的解决方案。