  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.
