vanlalruata/DCNNBiLSTM-An-Efficient-Hybrid-Deep-Learning-Based-Intrusion-Detection-System
Journal Article: Telematics and Informatics Reports
This system helps network administrators automatically identify and classify various cyberattacks in real-time network traffic. It takes raw network data as input and provides an alert classifying the type of intrusion detected. Network administrators and security analysts are the primary users who benefit from this improved ability to detect threats.
No commits in the last 6 months.
Use this if you need an automated, highly accurate system to detect and classify cyberattacks in your network traffic.
Not ideal if you require a system that can explain its detection reasoning or operate on highly specialized, custom network protocols without prior training.
Stars
12
Forks
1
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jun 18, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/vanlalruata/DCNNBiLSTM-An-Efficient-Hybrid-Deep-Learning-Based-Intrusion-Detection-System"
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