aidude/autoencoder_anomaly_detection
Experiment for detecting Anomaly detection using Autoencoders
This project helps network security professionals identify unusual and potentially malicious activity within their network traffic. It takes in large volumes of network data and flags entries that deviate significantly from normal patterns, helping to detect novel intrusions that traditional systems might miss. Security analysts and operations engineers would find this useful for monitoring network health and detecting threats.
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Use this if you need to detect unusual patterns or potential intrusions in large datasets, especially when you have very few examples of what an 'anomaly' looks like.
Not ideal if you need a real-time intrusion prevention system that immediately terminates connections or if you already have a robust, labeled dataset of known anomalies.
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Language
Python
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Last pushed
Jan 30, 2018
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