Iretha/IoT23-network-traffic-anomalies-classification
AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset
This project helps network security analysts detect and classify unusual or malicious activity within their IoT network traffic. It takes a large dataset of network flow records, processes it to isolate different types of traffic, and then applies machine learning models to identify and categorize anomalies. The output helps security teams understand what kind of threats are present in their IoT environment.
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Use this if you need to analyze large volumes of IoT network data to automatically identify and classify different types of security threats or unusual behaviors.
Not ideal if you need a real-time anomaly detection system for an active network, as this project is designed for batch processing and experimentation with pre-recorded datasets.
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92
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Language
Python
License
MIT
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Last pushed
Jul 30, 2021
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