Western-OC2-Lab / AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-SecurityLinks
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
☆36Updated 3 months ago
Alternatives and similar repositories for AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-Security
Users that are interested in AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-Security are comparing it to the libraries listed below
Sorting:
- This repository includes code for the paper "Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion D…☆26Updated 7 months ago
- Data stream analytics: Implement online learning methods to address concept drift and model drift in dynamic data streams. Code for the p…☆33Updated 2 years ago
- Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library…☆211Updated 2 years ago
- Code for intrusion detection system (IDS) development using CNN models and transfer learning☆169Updated 2 years ago
- Implementation of Robust Transformer Based Intrusion Detection, based on the Paper by Wu et. Al☆22Updated 9 months ago
- Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGB…☆504Updated last year
- The purpose of this repository is to demonstrate the steps of processing CICIDS2017 dataset using machine learning algorithms.☆63Updated 4 years ago
- As society and technology develop, more and more of our time is spent online, from shopping to socialising, working to banking. Ensuring …☆13Updated 2 years ago
- An Intrusion Detection System based on Deep Belief Networks☆83Updated 2 years ago
- Source code for paper "Multi-Classification In-Vehicle Intrusion Detection System using Packet- and Sequence-Level Characteristics from T…☆32Updated 2 months ago
- Network intrusion detection with Machine Learning (Deep Learning) experiment : 1d-cnn, softmax, neural networks, convolution☆46Updated last year
- Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems☆23Updated 4 years ago
- Adaptive Federated Learning Approach to DDoS attack detection☆48Updated 6 months ago
- This repository contains an in-depth analysis of the Intrusion Detection Evaluation Dataset (CIC-IDS2017) for Intrusion Detection, showca…☆70Updated last year
- Deep learning models for network intrusion detection☆37Updated 2 years ago
- ☆42Updated 2 years ago
- CICIDS2017 dataset☆70Updated 3 years ago
- ☆20Updated 5 months ago
- Our implementations of the flow-based network intrusion detection model (for the COMNET paper)☆46Updated 5 years ago
- A Few-shot Based Model-Agnostic Meta-Learning for Intrusion Detection in Security of Internet of Things☆14Updated 4 months ago
- Journal Article: Telematics and Informatics Reports☆12Updated 2 years ago
- FL-based intrusion detection system development using model averaging.☆38Updated 2 years ago
- Code for the paper "Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models"☆33Updated 2 years ago
- ☆84Updated last year
- ⼀个有潜⼒的⽹络⼊侵检测算法，引⼊了乘法注意⼒机制将其融合到 CNN 的分类模型中，在KDD99和UNSM-NB15数据集进行模型的训练结果优于一般机器学习方法和CNN。A promising network intrusion detection algorithm is…☆10Updated last year
- Repository for IEEE CCNC'21 paper titled "Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network".☆46Updated last year
- An online learning method used to address concept drift and model drift. Code for the paper entitled "A Lightweight Concept Drift Detecti…☆51Updated last year
- ☆21Updated 8 months ago
- This is a simple DNN based application for an IDS based on the CICIDS2017 dataset☆33Updated 4 years ago
- Pre-processing NSL-KDD dataset using Data mining techniques. Algorithm written in python to detect the attacks in NSL KDD dataset.☆26Updated 5 years ago