Where do we start?

After the project presentation, we will start by making a HelloWorld test prototype. The purpose of a prototype machine learning model would be to identify an input value message based on which log type the value message is and return log level as an output value.

Goal

The log information is sorted into 0-7 different log levels for example. Hypervisor detected: “Hypervisor detected: KVM”,”PRIORITY”:6″ so, its informative level 6 log information which tells that KVM Hypervisor detected from the computer. We start by teaching the machine learning model a lot of this type of data from different log levels.

0KERN_EMERGEmergency condition, system is probably dead
1KERN_ALERTSome problem has occurred, immediate attention is needed
2KERN_CRITA critical condition
3KERN_ERRAn error has occurred
4KERN_WARNINGA warning
5KERN_NOTICENormal message to take note of
6KERN_INFOSome information
7KERN_DEBUGDebug information related to the program
Different log levels classified source: https://en.wikipedia.org/wiki/Printk

The idea would be to have the final model identify the feed value from the message “Hypervisor detected: KVM” that this log belongs to informative level 6.

Image: Example of basic neural network model.

The model shown in the figure has an input value of 1, based on which the machine learning model calculates the most likely result and gives its output as 0-7. 1 input value of this model will be the informative message and 7 output values will be the log level for that message.

Sources:

Wikipedia, Artificial neural network: https://en.wikipedia.org/wiki/Artificial_neural_network

Wikipedia, printk https://en.wikipedia.org/wiki/Printk

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