DDoS-Detection and DetectingDDosAttacks

DDoS-Detection
30
Emerging
DetectingDDosAttacks
25
Experimental
Maintenance 0/25
Adoption 6/25
Maturity 8/25
Community 16/25
Maintenance 0/25
Adoption 4/25
Maturity 8/25
Community 13/25
Stars: 15
Forks: 7
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: —
Stars: 8
Forks: 2
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: —
No License Stale 6m No Package No Dependents
No License Stale 6m 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 DetectingDDosAttacks

kedarghule/DetectingDDosAttacks

The project implements and tests various machine learning algorithms to better classify and detect DDoS attacks.

This project helps network security analysts automatically identify Distributed Denial of Service (DDoS) attacks. It takes network traffic data, including IP addresses and timestamps, and classifies it as either a normal benign connection or a DDoS attack. This tool is designed for network defenders and security operations center (SOC) personnel.

network-security DDoS-detection threat-intelligence cybersecurity-operations network-monitoring

Related comparisons

DDoS-Detection and DDos-Detection-using-ML DDoS-Detection and DDoS-Detection-ML DDoS-Detection and Machine-Learning-Based-Detection-of-Distributed-Denial-of-Service-DDoS-Attacks DDoS-Detection and DDoS-Detection

Scores updated daily from GitHub, PyPI, and npm data. How scores work