othmbela/dbn-based-nids
An Intrusion Detection System based on Deep Belief Networks
This project offers a specialized intrusion detection system designed to identify various types of network attacks. It takes raw network traffic data, processes it, and then classifies suspicious activities, helping security analysts understand and respond to threats. Network security analysts or operations engineers responsible for monitoring network health and detecting malicious activity would find this useful.
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Use this if you are a cybersecurity professional who needs to analyze network traffic for multiple types of intrusions using a deep learning approach and have access to labeled network datasets for training.
Not ideal if you need an out-of-the-box, plug-and-play solution without any technical setup or if you lack expertise in Python and machine learning model training.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Oct 27, 2022
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