This is a five-step framework for the development of intrusion detection systems (IDS) using machine learning (ML) considering model realization, and performance evaluation.

Last update: Jan 04, 2023

Overview

AB-TRAP: building invisibility shields to protect network devices

The AB-TRAP framework is applicable to the development of Network Intrusion Detection Systems (NIDS), it enables the use of updated network traffic and considers operational concerns to enable the complete deployment of the solution. It is a five-step framework consisting of (i) the generation of the attack dataset, (ii) the bonafide dataset, (iii) training of machine learning models, (iv) realization of the models, and (v) the performance evaluation of the realized model after deployment.

This repositories contains the examples for both Local Area Network (LAN), and the Internet environment taking advantage of virtualization (virtual machines and containers) to support the dataset generation.

This repository contains all the necessary files to rebuilt this project.

Content of this repository
Pre-requisites
Contribute to the framework
How to cite

Content of this repository

/1_Attack dataset: contains the instructions and the required code to generate the attack dataset considering both LAN and Internet environment;
/2_Bonafide dataset: contains the instructions and the required code to generate the bonafide dataset based on the MAWILab dataset;
/3_Training models: contains the Jupyter Notebooks to pre-process the data, and generate the ML models (LAN and Internet cases);
/4_RealizAtion: contains the source code to obtain the machine learning models to be embedded on the target devices, both in the kernel-space using LKM (LAN case), and user-space with Python language (Internet case);
/5_Performance Evaluation: contains the instructions to evaluate the Performance of machine learning models in the target device;

Pre-requisites

For the host computer, it is required Python language with the dependencies listed in requirements.txt.

You can setup the environment with Python packet manager (pip):

$ pip install -r requirements.txt

The target computer used on this work is the Raspberry Pi 4.

Contribute to the framework

To contribute with the framework, you can use the Issues and Pull Requests from Github platform.

How to cite

@ARTICLE{9501960,  
  author={De Carvalho Bertoli, Gustavo and Pereira Júnior, Lourenço Alves and Saotome, Osamu and Dos Santos, Aldri L. 
        and Verri, Filipe Alves Neto and Marcondes, Cesar Augusto Cavalheiro and Barbieri, Sidnei and Rodrigues, Moises S. 
        and Parente De Oliveira, José M.},  
  journal={IEEE Access},   
  title={An End-to-End Framework for Machine Learning-Based Network Intrusion Detection System},   
  year={2021},  
  volume={9},  
  number={},  
  pages={106790-106805},  
  doi={10.1109/ACCESS.2021.3101188}
}

Comments

Simple ROC Analysis.

I performed a simple ROC analysis in the chosen model.

One still needs to choose the appropriate thresholds/goals and generate the plots for the paper.

opened by verri 0

Releases(v0.1.0)

v0.1.0(Jul 15, 2021)

Initial pre-release without public API based on the semantic proposed by semver.org
Source code(tar.gz)
Source code(zip)

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

What is xLearn? xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machin

2.8k Feb 12, 2021

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

chitra What is chitra? chitra (चित्र) is a multi-functional library for full-stack Deep Learning. It simplifies Model Building, API development, and M

210 Dec 21, 2022

An efficient PyTorch implementation of the evaluation metrics in recommender systems.

recsys_metrics An efficient PyTorch implementation of the evaluation metrics in recommender systems. Overview • Installation • How to use • Benchmark

12 Dec 2, 2022

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

14.5k Jan 8, 2023

Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

7 Nov 27, 2022

The project covers common metrics for super-resolution performance evaluation.

Super-Resolution Performance Evaluation Code The project covers common metrics for super-resolution performance evaluation. Metrics support The script

10 Aug 3, 2022

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

H2O H2O is an in-memory platform for distributed, scalable machine learning. H2O uses familiar interfaces like R, Python, Scala, Java, JSON and the Fl

6.1k Jan 5, 2023

A Data Annotation Tool for Semantic Segmentation, Object Detection and Lane Line Detection.(In Development Stage)

Data-Annotation-Tool How to Run this Tool? To run this software, follow the steps: git clone https://github.com/Autonomous-Car-Project/Data-Annotation

13 Aug 18, 2022

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

AutoTrader AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading systems. From sim

485 Jan 9, 2023

This is a five-step framework for the development of intrusion detection systems (IDS) using machine learning (ML) considering model realization, and performance evaluation.

Related tags

Overview

AB-TRAP: building invisibility shields to protect network devices

Content of this repository

Pre-requisites

Contribute to the framework

How to cite

You might also like...

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

An efficient PyTorch implementation of the evaluation metrics in recommender systems.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

The project covers common metrics for super-resolution performance evaluation.

A Data Annotation Tool for Semantic Segmentation, Object Detection and Lane Line Detection.(In Development Stage)

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

Comments

Simple ROC Analysis.

Releases(v0.1.0)

v0.1.0(Jul 15, 2021)

Owner

Lab-C2DC - Laboratory of Command and Control and Cyber-security

This program writes christmas wish programmatically. It is using turtle as a pen pointer draw christmas trees and stars.

Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility ICCV2021

An investigation project for SISR.

Official Repository for our ECCV2020 paper: Imbalanced Continual Learning with Partitioning Reservoir Sampling

Self-Regulated Learning for Egocentric Video Activity Anticipation

Boosted CVaR Classification (NeurIPS 2021)

The Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

Remote sensing change detection tool based on PaddlePaddle

pyhsmm - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.

A motion detection system with RaspberryPi, OpenCV, Python

StyleGAN2 - Official TensorFlow Implementation

SmallInitEmb - LayerNorm(SmallInit(Embedding)) in a Transformer to improve convergence

A tool to visualise the results of AlphaFold2 and inspect the quality of structural predictions

A Low Complexity Speech Enhancement Framework for Full-Band Audio (48kHz) based on Deep Filtering.

AI Face Mesh: This is a simple face mesh detection program based on Artificial intelligence.

The official implementation of Equalization Loss for Long-Tailed Object Recognition (CVPR 2020) based on Detectron2

Fast convergence of detr with spatially modulated co-attention

Faster Convex Lipschitz Regression

“袋鼯麻麻——智能购物平台”能够精准地定位识别每一个商品

Implementation of " SESS: Self-Ensembling Semi-Supervised 3D Object Detection" (CVPR2020 Oral)