DDoS-Detection and Machine-Learning-Based-Detection-of-Distributed-Denial-of-Service-DDoS-Attacks
Maintenance
0/25
Adoption
6/25
Maturity
8/25
Community
16/25
Maintenance
6/25
Adoption
3/25
Maturity
13/25
Community
0/25
Stars: 15
Forks: 7
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: —
Stars: 4
Forks: —
Downloads: —
Commits (30d): 0
Language: —
License: MIT
No License
Stale 6m
No Package
No Dependents
No Package
No Dependents
About DDoS-Detection
AnshumanMohanty-2001/DDoS-Detection
Detailed Comparative analysis of DDoS detection using Machine Learning Models
This project helps network security professionals and system administrators identify Distributed Denial of Service (DDoS) attacks. It takes network traffic data (like packet information) and uses various machine learning techniques to determine if an attack is underway. The output is a classification indicating whether the network activity is normal or part of a DDoS attack.
network-security
cybersecurity
intrusion-detection
network-monitoring
threat-analysis
About Machine-Learning-Based-Detection-of-Distributed-Denial-of-Service-DDoS-Attacks
acetinkaya/Machine-Learning-Based-Detection-of-Distributed-Denial-of-Service-DDoS-Attacks
Machine Learning-Based Detection of Distributed Denial-of-Service (DDoS) Attacks
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work