Data Classifier | Utilities
Classify and categorize your data by extracting relevant information from a body of text, log or code block.
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The prompt is the main searchterm provided to the tool. This needs to be in a format required by the tool otherwise an error may be returned. For example some tools may require URLs while others may require IP addresses.
Some options may be required while others are optional. If there are no options, an empty object should be provided.
The default API key is provided as an example only. This will not work in your own applications. Register for a free account to get an API key.
Example CURL Request
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Data Classifier
Token cost: 1Enter a body of text, log or code block to extract relevant information and classify it into categories.
Entropy is a useful analysis technique in cyber security as it can be used to identify encrypted or compressed data, detect malware, and identify data types. It is a measure of the uncertainty or information content in a set of data. It quantifies the average amount of information needed to describe the data. The entropy value ranges from 0 to 7. When the entropy is 0, it means the data is perfectly predictable or has no uncertainty, as all the values in the dataset are the same. Conversely, when the entropy is 7, it indicates maximum uncertainty or randomness, where all possible values are equally likely to occur. In general, higher entropy values signify greater complexity or information diversity in the dataset.
Shannon entropy, named after mathematician Claude Shannon, is a measure of the uncertainty or information content in a set of data. It quantifies the average amount of information needed to describe the data. The entropy value ranges from 0 to 7. When the entropy is 0, it means the data is perfectly predictable or has no uncertainty, as all the values in the dataset are the same. Conversely, when the entropy is 7, it indicates maximum uncertainty or randomness, where all possible values are equally likely to occur. In general, higher entropy values signify greater complexity or information diversity in the dataset.
The block size is the number of characters used to calculate the entropy of the input data. For example, if the block size is 8, the input data is divided into blocks of 8 characters each, and the entropy is calculated for each block.
