IndexFziQ/ML-ATIC
Abnormal Traffic Identification Classifier based on Machine Learning. My code for undergraduate graduation design.
This project helps network security analysts or students in network defense identify unusual patterns in network traffic. It takes raw network flow data, specifically from datasets like KDDCUP99, and processes it to detect abnormal activities. The output is a classification of network events, flagging what might be a security threat or anomaly.
No commits in the last 6 months.
Use this if you are a student or researcher exploring machine learning methods for network intrusion detection and want to experiment with a pre-built classifier.
Not ideal if you need a robust, production-ready system for real-time network anomaly detection or if you require extensive support and frequent updates.
Stars
30
Forks
9
Language
Java
License
MIT
Category
Last pushed
Nov 25, 2020
Commits (30d)
0
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