DDoS-Detection and DetectingDDosAttacks

DDoS-Detection
32
Emerging
DetectingDDosAttacks
25
Experimental
Maintenance 0/25
Adoption 7/25
Maturity 8/25
Community 17/25
Maintenance 0/25
Adoption 4/25
Maturity 8/25
Community 13/25
Stars: 31
Forks: 9
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

ReubenJoe/DDoS-Detection

Detailed Comparative analysis of DDoS detection using Machine Learning Models

This project helps network security teams and operations engineers identify Distributed Denial of Service (DDoS) attacks. It takes network traffic data as input and uses various machine learning models to classify whether the traffic is normal or part of a DDoS attack. The output helps security professionals quickly detect and respond to these critical threats.

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

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