AI-based Log Rate Anomaly Detection.

Jun 9, 2021. By Aleena Mathew

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AI-based Log Rate Anomaly Detection

Modern IT systems are evolving at a large scale and speed. Also with digital transformation at its peak, IT organizations are trying to scale up to reach the best. This might involve developing multiple applications or software that is required to catch up. Having these applications or software is a high requirement for the organizations uprun. But the major challenge here is in observing and monitoring these applications 24x7. As the systems get bigger and complex, the amount of data generated will be huge. And from this huge volume of data generated IT operators need to identify the metrics and logs and then manually analyze and identify insights from these data. Every developer relies on the logs that are generated from these applications to identify if there is any errors in the application or software or if there is an occurrence of abnormal activity in the application such as high user transaction rates. Manual identification and processing take a lot of time and also processing every log and correlating them to identify the issue is not easy.

Moreover, IT organizations are being more advanced and sophisticated with the use of high-end models such as distributed microservice architecture. This change will just add on the volume of the log to be monitored and this is a hectic task. So we need a solution here which is capable enough to monitor every incoming log and automatically identify any outliers in them. That's where artificial intelligence comes into action.

AI-based Log Anomaly Detection:

As discussed above, with the upcome of new technologies and the continuous competition in IT organization, the need for using these technologies and developing new applications became a must. This change added on the incoming logs and monitoring them created big chaos. That's where the implementation of AI-based log analysis came into the picture. The AI-based log analysis helped in automatically analyzing the incoming log. Those logs from every application will be automatically be captured by the AI-based systems. for monitoring the logs from multiple applications. With an AI-based system, every application and software gets the capability of having a single platform for monitoring their entire logs. So we have seen how efficiently application and software logs can be collected. The next part is automatically identifying any unknown problems from these log entries. That's where log anomaly detection plays its part. The anomaly detection mechanism uses advanced machine learning and artificial intelligence technologies which are capable of identifying unknown problems automatically. In the case of log data collected, a similar anomaly detection mechanism will take place, in which the log rate is calculated. In the log rate anomaly detection, the rate at which a log is generated will be calculated. Based on this rate calculation it is identified that if a threshold breach has occurred in the log rate entry. Any log rate breach can be determined if there has been a threshold breach on a log entry or if there is any template match with the corresponding breach. Based on the identification of the anomaly by the AI-based models, the next phase is to auto-remediate the issue. The AI-based models will automatically identify the log issue, and then perform an auto-remediation solution to resolve the issue. In this way, IT operators do not need to worry about how to fix an issue or waste time by identifying a solution for it. Let's see how this scenario works with an example.

Usecase for Log rate anomaly detection:

‌There are high scenarios in which users may access the application and do certain transactions higher than the normal rate. This can lead to situations where there is a usual behavior in the application transaction rate. This can be due to any reason and can lead to application failure too. Manually debugging the application failure is not possible as IT operators need to monitor huge volumes of the log entry. There can be a delay in the process and this delay can lead to bigger issues that may affect the end-user side too. The right solution here is to apply AIOps. ‌AIOps platforms enable automatically detecting and capturing the issue/attack right at the time of cause. This will proactively alert the IT operators and an auto-remediation action will take place to block the user from further attack.

To learn more about Algomox AIOps, please visit our AIOps Platform Page.

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