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

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility

RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

Face recognition project by matching the features extracted using SIFT.

This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

A deep-learning pipeline for segmentation of ambiguous microscopic images.

Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

Sudoku solver - A sudoku solver with python

An Intelligent Self-driving Truck System For Highway Transportation

CLIPort: What and Where Pathways for Robotic Manipulation

Official repository for "Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems"

This is an open solution to the Home Credit Default Risk challenge 🏡

Implementation of Bottleneck Transformer in Pytorch

An Abstract Cyber Security Simulation and Markov Game for OpenAI Gym

Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging

Evaluation framework for testing segmentation networks in PyTorch

On the adaptation of recurrent neural networks for system identification

This repository contains the code used in the paper "Prompt-Based Multi-Modal Image Segmentation".

Transfer Learning library for Deep Neural Networks.

Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.