Comparison of F1 score.

<div><p>Virtual machine logs are generated in large quantities. Virtual machine logs may contain some abnormal logs that indicate security risks or system failures of the virtual machine platform. Therefore, using unsupervised anomaly detection methods to identify abnormal logs is a mean...

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Bibliographic Details
Main Author:	Hao Zhang (15339) (author)
Other Authors:	Yun Zhou (308977) (author), Huahu Xu (20521389) (author), Jiangang Shi (402247) (author), Xinhua Lin (538781) (author), Yiqin Gao (5236673) (author)
Published:	2025
Subjects:	Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified virtual machine platform support vector machine strong sequential patterns method called ladsvm indicate security risks feature extraction algorithm experimental results demonstrate dimensional latent space virtual machine logs log parsing algorithm performance may vary ladsvm approach excels extract key features combines long short identify abnormal logs disordered log data long short extract features parsing logs abnormal logs log information dimensional input traditional methods term memory system failures svm ). specific characteristics real world prior knowledge paper proposes often challenging meaningful task large quantities increase robustness detecting anomalies anomaly detection anomaly alerts
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Comparison of F1 score.

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