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their own honeypots as part of the quarantine environment. Users just have to forward the honeypot's HTTP requests to the environment's backend server (backend processes will automatically scrub and forward data to the analysis dashboard - ELK stack). 3. The Library - Retraining Sequence to Reinforce the Brains | Smart isn't really smart till you can keep learning. As new payloads and attack vectors emerge, it is important to upgrade detection capabilities in order to ensure security. Hence, a retraining function is built into NGWAF to ensure defenders are able to train the machine learning model to detect those newer payloads. Retraining of datasets is one of the main features in NGWAF. On our dashboard, users can insert new dataset for retraining, to strengthen and improve the quality of NGWAF detection of malicious payloads.

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This can be achieved in the following steps: Create a new dataset (.csv) for upload in the following format (empty column, training data, label). You can refer to patch_sqli.csv as an example. Navigate to http://localhost:8088 to view NGWAF admin panel. Select the "Import Dataset" tab and upload the training set you have created

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Confirm that the training set have been uploaded successfully under the "Manage Datasets" tab.

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Under "Manage Model" tab, select the dataset(s) you want to retrain the model on and click on the "UPDATE WAF MODEL" button. 

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Congrats! The model should finish re-training after some time.

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4. Additional Features: NGWAF uses ELK stack to capture logs of network data that passes through NGWAF, allowing users to monitor the traffic that passes through the NGWAF for further analysis.

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