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network-anomaly

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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

yousefkotp / Network-Anomaly-Detection

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This project focuses on network anomaly detection due to the exponential growth of network traffic and the rise of various anomalies such as cyber attacks, network failures, and hardware malfunctions. This project implement clustering algorithms from scratch, including K-means, Spectral Clustering, Hierarchical Clustering, and DBSCAN

clustering k-means anomaly clustering-algorithm dbscan hierarchical-clustering density-based-clustering anomaly-detection dbscan-clustering unsupervised-machine-learning dendogram spectral-clustering k-means-implementation-in-python k-means-clustering unsupervised-clustering spectral-embedding anomaly-detection-algorithm db-scan anomaly-clustering network-anomaly
  • Updated Jun 11, 2023
  • Jupyter Notebook

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