jay-johnson/antinex-core
Network exploit detection using highly accurate pre-trained deep neural networks with Celery + Keras + Tensorflow + Redis
This project helps cybersecurity professionals or network administrators automatically detect network exploits by processing network traffic data. You feed it historical network attack datasets, and it produces highly accurate deep neural network models capable of identifying future attacks on various application servers like Django, Flask, React, Vue, and Spring. The core users are those responsible for protecting web applications from cyber threats.
No commits in the last 6 months. Available on PyPI.
Use this if you need an automated system to train and deploy deep learning models for real-time network exploit detection on common web application servers.
Not ideal if you are looking for a standalone, out-of-the-box network intrusion detection system without requiring integration with a broader stack or custom training.
Stars
22
Forks
2
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Dec 07, 2018
Commits (30d)
0
Dependencies
32
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/jay-johnson/antinex-core"
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
Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning
Code for intrusion detection system (IDS) development using CNN models and transfer learning