GitHub topics: network-anomaly-detection
cybersecurity-dev/awesome-network-anomaly-detection
Awesome Network Anomaly Detection
Size: 7.81 KB - Last synced at: 3 days ago - Pushed at: 20 days ago - Stars: 1 - Forks: 0
sand-ci/ps-dash
A dashboard which tracks the alarms and alerts, statuses and metrics of the WLCG/OSG sites
Language: Python - Size: 4.98 MB - Last synced at: 30 days ago - Pushed at: 30 days ago - Stars: 0 - Forks: 2
Projects-Developer/Network-Anomaly-Detection-System-Project-Machine-Learning-Project
Project designed to identify unusual patterns or activities in network traffic that could indicate potential security threats, such as attacks, intrusions, or breaches. Network Anomaly Detection System Using Machine Learning With Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
Size: 8.79 KB - Last synced at: 4 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0
Projects-Developer/10-Latest-Final-Year-Projects-with-Source-Code
10 Latest Final Year Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials
Size: 6.84 KB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 3 - Forks: 1
arhcoder/QuintuSCAN
🧱 [🏅TALENT LAND HACKATHON FINALIST] Desktop system with Artificial Intelligence to detect cybersecurity attacks in network; also considering the prevention of phishing and scam.
Language: Jupyter Notebook - Size: 131 MB - Last synced at: 3 months ago - Pushed at: over 1 year ago - Stars: 3 - Forks: 0
jvmolu/Network-Anomaly-Detection
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.
Language: Jupyter Notebook - Size: 4.28 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
MohEsmail143/network-anomaly-detection
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.
Language: Jupyter Notebook - Size: 18.5 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
Youhanna-Yousry/Network-Anomaly-Detection
This project compares between different clustering algorithms: K-Means, Normalized Cut and DBSCAN algorithms for network anomaly detection on the KDD Cup 1999 dataset
Language: Jupyter Notebook - Size: 306 KB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0
