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Example

Removing duplicate entries and normalizing IP address formats in network traffic logs.

Scenario

In a scenario where a cybersecurity team collects extensive logs from their network, Lumiere can identify and remove duplicate data entries, standardize IP address formats, and fill in missing values, thereby preparing the dataset for effective anomaly detection analysis.

Example

Extracting features such as the frequency of access attempts, types of requests, and source country from web server logs.

Scenario

For a web application facing frequent unauthorized access attempts, Lumiere can analyze server logs to extract meaningful features that help in identifying patterns, such as increased access attempts from specific countries, pointing towards potential cybersecurity threats.

Example

Creating labeled datasets for training intrusion detection systems (IDS) by classifying network behaviors as 'normal' or 'malicious'.

Scenario

In developing an IDS, Lumiere can take network traffic data, apply labels based on predefined criteria (e.g., signatures of known attacks), and generate datasets that are used to train ML models to detect future intrusions.