buseyaren/classification-and-detection-ddosattacks
In this repository, DDOS attacks were detected using Recurrent Neural Networks (LSTM) and Classical Machine Learning Algorithms.
This project helps network administrators and security professionals identify and prevent Distributed Denial of Service (DDoS) attacks. It takes network traffic data as input and outputs predictions of whether an active DDoS attack is occurring. Those responsible for maintaining network uptime and security would use this.
No commits in the last 6 months.
Use this if you need to automatically detect DDoS attacks to protect your network infrastructure and ensure continuous service.
Not ideal if you are looking for a complete network security solution that includes prevention, mitigation, and incident response beyond just detection.
Stars
19
Forks
3
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 28, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/buseyaren/classification-and-detection-ddosattacks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
santhisenan/DeepDefense
DDoS attack detection using BLSTM based RNN
GAR-Project/project
DDoS attacks detection by using SVM on SDN networks.
MeherRushi/FlowSentryX
FlowSentryX is an open-source XDP-based fast packet processing DOS and DDOS Mitigation Framework...
ReubenJoe/DDoS-Detection
Detailed Comparative analysis of DDoS detection using Machine Learning Models
ash0545/sdn-ml-ids
SDN Topology Emulation and Development of Dataset for ML-Based Intrusion Detection through the...