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network-anomaly-detection

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MohEsmail143 / network-anomaly-detection

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An attempt at the network anomaly detection task using manually implemented k-means, spectral clustering and DBSCAN algorithms, with manually implemented evaluation metrics (precision, recall, f1-score and conditional entropy) used to evaluate these algorithms.

python machine-learning jupyter-notebook recall k-means unsupervised-learning normalized-cuts precision dbscan f1-score dbscan-clustering unsupervised-machine-learning spectral-clustering spectral-clustering-algorithm k-means-clustering conditional-entropy network-anomaly network-anomaly-detection
  • Updated Mar 13, 2024
  • Jupyter Notebook

jvmolu / Network-Anomaly-Detection

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Explore Network Anomaly Detection Project 📊💻. It achieves an exceptional 99.7% accuracy through a blend of supervised and unsupervised learning, extensive feature selection, and model experimentation. Stunning data visualizations using synthetic network traffic data offer insightful representations of anomalies, enhancing network security.

deep-learning machine-learning-algorithms python3 feature-engineering auto-encoders network-anomaly-detection
  • Updated Apr 6, 2024
  • Jupyter Notebook

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