sinanw/ml-classification-malicious-network-traffic
This project aims to analyze and classify a real network traffic dataset to detect malicious/benign traffic records. It compares and tunes the performance of several Machine Learning algorithms to maintain the highest accuracy and lowest False Positive/Negative rates.
No commits in the last 6 months.
Stars
24
Forks
5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sinanw/ml-classification-malicious-network-traffic"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
AIS-Package/aisp
Artificial Immune Systems Package (AISP) is an open-source Python library that features...
ubc-provenance/PIDSMaker
A framework for building provenance-based intrusion detection systems with neural networks
Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning
Code for IDS-ML: intrusion detection system development using machine learning algorithms...
zimingttkx/Network-Security-Based-On-ML
基于机器学习的网络安全检测系统 | 集成Kitsune/LUCID算法 | 支持ML/DL/RL模型 | 99.58%攻击检测准确率 | 19913 QPS | Docker/K8s部署
abhinav-bhardwaj/Network-Intrusion-Detection-Using-Machine-Learning
A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach