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Aug 6th, 2022
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How to Standardize Amount Log For Free

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Defenders welcome back in this video Im going to show you guys how we can normalize our logs so that we can create Universal field names that we can use to create Universal dashboards and also Universal alerts no matter of the log source that we are ingesting logs from the current issue that we are facing is that lets say for example looking at our adjusted logs here from my Windows endpoint you see connected to destination IP addresses is written to this data underscore when underscore event data underscore destination IP where if I look at the same type of log from my Linux endpoint so this is still a network connection that was invoked by one of my Linux endpoints you see our destination IP field is slightly different it is written to a data underscore event data underscore destination IP and that gives us a problem when creating Universal dashboards or running quick searches across our environment to see okay what maybe endpoints connected to in our example destination IP 8.8.4.

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What is Normalization? It is a scaling technique method in which data points are shifted and rescaled so that they end up in a range of 0 to 1. It is also known as min-max scaling.
Optimize Logs for Write or for Read There are two ways to implement search on data: Store the data in a structured way, so that queries are run relatively fast, although the storing itself is slower. Store the data as it is, in order to make storing quite fast, but making querying slower as trade-off.
Normalize Data with Min-Max Scaling in R Another efficient way of Normalizing values is through the Min-Max Scaling method. With Min-Max Scaling, we scale the data values between a range of 0 to 1 only. Due to this, the effect of outliers on the data values suppresses to a certain extent.
Here are the steps to use the normalization formula on a data set: Calculate the range of the data set. Subtract the minimum x value from the value of this data point. Insert these values into the formula and divide. Repeat with additional data points.
How to Normalize Data Between 0 and 1 To normalize the values in a dataset to be between 0 and 1, you can use the following formula: zi = (xi min(x)) / (max(x) min(x)) where: For example, suppose we have the following dataset: The minimum value in the dataset is 13 and the maximum value is 71.
Normalization using Min Max Values Here normalization of data can be done by subtracting the data with the minimum value in the data and dividing the result by the difference between the maximum value and the minimum value in the given data.
The syntax of STANDARDIZE in Excel is as follows: =STANDARDIZE(x,mean,standarddeviation) x is the input value to be standardized. mean is the average of the input values. standarddeviation is the standard deviation of the input values.
Standardize your logs This will help avoid situations in which the same log message is assigned the same severity or where critical events go unnoticed because of the wrong severity assigned to it.
Normalize Data with Min-Max Scaling in R Another efficient way of Normalizing values is through the Min-Max Scaling method. With Min-Max Scaling, we scale the data values between a range of 0 to 1 only. Due to this, the effect of outliers on the data values suppresses to a certain extent.
Standardization: Standardizing the features around the center and 0 with a standard deviation of 1 is important when we compare measurements that have different units. Variables that are measured at different scales do not contribute equally to the analysis and might end up creating a bais.

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