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首页> 外文会议>IFIP WG 12.5 International workshops on artificial intelligence applications and innovations;Mining Humanistic Data Workshop;Workshop on 5G-Putting Intelligence to the Network Edge >Boosted Ensemble Learning for Anomaly Detection in 5G RAN
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Boosted Ensemble Learning for Anomaly Detection in 5G RAN

机译：用于5G RAN中异常检测的增强集成学习

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摘要

The emerging 5G networks promises more throughput, faster, and more reliable services, but as the network complexity and dynamics increases, it becomes more difficult to troubleshoot the systems. Vendors are spending a lot of time and effort on early anomaly detection in their development cycle and majority of the time is spent on manually analyzing system logs. While main research in anomaly detection uses performance metrics, anomaly detection using functional behaviour is still lacking in depth analysis. In this paper we show how a boosted ensemble of Long Short Term Memory classifiers can detect anomalies in the 5G Radio Access Network system logs. Acquiring system logs from a live 5G network is difficult due to confidentiality issues, live network disturbance, and problems to repeat scenarios. Therefore, we perform our evaluation on logs from a 5G test bed that simulate realistic traffic in a city. Our ensemble learns the functional behaviour of an application by training on logs from normal execution time. It can then detect deviations from normal behaviour and also be retrained on false positive cases found during validation. Anomaly detection in RAN shows that our ensemble called BoostLog, outperforms a single LSTM classifier and further testing on HDFS logs confirms that BoostLog also can be used in other domains. Instead of using domain experts to manually analyse system logs, BoostLog can be used by less experienced trouble shooters to automatically detect anomalies faster and more reliable.
机译：新兴的5G网络有望提供更高的吞吐量，更快和更可靠的服务，但是随着网络复杂性和动态性的提高，对系统进行故障排除变得更加困难。供应商在其开发周期中花费了大量时间和精力进行早期异常检测，并且大部分时间都花在了手动分析系统日志上。尽管异常检测的主要研究使用性能指标，但在深度分析中仍缺乏使用功能行为的异常检测。在本文中，我们展示了增强的长期短期记忆分类器集合如何检测5G无线电访问网络系统日志中的异常。由于机密性问题，实时网络干扰以及重复场景的问题，很难从实时5G网络获取系统日志。因此，我们对来自模拟城市实际流量的5G测试台的日志进行评估。我们的团队通过从正常执行时间开始对日志进行培训来学习应用程序的功能行为。然后，它可以检测到与正常行为的偏差，并且还可以对验证期间发现的假阳性案例进行重新培训。 RAN中的异常检测表明，我们称为BoostLog的集合优于单个LSTM分类器，对HDFS日志的进一步测试证实了BoostLog也可以在其他领域中使用。经验丰富的故障排除人员可以使用BoostLog而不是使用领域专家来手动分析系统日志，而是可以更快，更可靠地自动检测异常。

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