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

Deep Learningmachine-learningintrusion-detection-systemsab-trap
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

  • /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}
}
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.
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

Chao Ma 2.8k Feb 12, 2021
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
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

Aniket Maurya 210 Dec 21, 2022
An efficient PyTorch implementation of the evaluation metrics in recommender systems.
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

Xingdong Zuo 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

Microsoft 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.
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

Abdultawwab Safarji 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

xmy 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

H2O.ai 6.1k Jan 5, 2023
A Data Annotation Tool for Semantic Segmentation, Object Detection and Lane Line Detection.(In Development Stage)
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

TiVRA AI 13 Aug 18, 2022
A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.
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

Kieran Mackle 485 Jan 9, 2023
Comments
  • Simple ROC Analysis.

    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)
Owner
Lab-C2DC - Laboratory of Command and Control and Cyber-security
Lab-C2DC - Laboratory of Command and Control and Cyber-security
GitHub Repository
Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

Deep Hedging Demo Pricing Derivatives using Machine Learning 1) Jupyter version: Run ./colab/deep_hedging_colab.ipynb on Colab. 2) Gui version: Run py

Yu Man Tam 102 Jan 06, 2023
Explanatory Learning: Beyond Empiricism in Neural Networks

Explanatory Learning This is the official repository for "Explanatory Learning: Beyond Empiricism in Neural Networks". Datasets Download the datasets

GLADIA Research Group 10 Dec 06, 2022
Source code of D-HAN: Dynamic News Recommendation with Hierarchical Attention Network

D-HAN The source code of D-HAN This is the source code of D-HAN: Dynamic News Recommendation with Hierarchical Attention Network. However, only the co

30 Sep 22, 2022
AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Confidence-based Graph Convolutional Networks for Semi-Supervised Learning Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Ne

MALL Lab (IISc) 56 Dec 03, 2022
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

Neural Scene Flow Fields PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes", CVPR 2021 [Projec

Zhengqi Li 583 Dec 30, 2022
Video lie detector using xgboost - A video lie detector using OpenFace and xgboost

video_lie_detector_using_xgboost a video lie detector using OpenFace and xgboost

2 Jan 11, 2022
The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

AICITY2021_Track2_DMT The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop. Introduction

Hao Luo 91 Dec 21, 2022
Bot developed in Python that automates races in pegaxy.

español | português About it: This is a fork from pega-racing-bot. This bot, developed in Python, is to automate races in pegaxy. The game developers

4 Apr 08, 2022
A Structured Self-attentive Sentence Embedding

Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR

Kaushal Shetty 488 Nov 28, 2022
Video-face-extractor - Video face extractor with Python

Python face extractor Setup Create the srcvideos and faces directories Put your

2 Feb 03, 2022
Tutel MoE: An Optimized Mixture-of-Experts Implementation

Project Tutel Tutel MoE: An Optimized Mixture-of-Experts Implementation. Supported Framework: Pytorch Supported GPUs: CUDA(fp32 + fp16), ROCm(fp32) Ho

Microsoft 344 Dec 29, 2022
Multi-Content GAN for Few-Shot Font Style Transfer at CVPR 2018

MC-GAN in PyTorch This is the implementation of the Multi-Content GAN for Few-Shot Font Style Transfer. The code was written by Samaneh Azadi. If you

Samaneh Azadi 422 Dec 04, 2022
An image processing project uses Viola-jones technique to detect faces and then use SIFT algorithm for recognition.

Attendance_System An image processing project uses Viola-jones technique to detect faces and then use LPB algorithm for recognition. Face Detection Us

8 Jan 11, 2022
An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics.

Sketch Simulator An architecture that makes any doodle realistic, in any specified style, using VQGAN, CLIP and some basic embedding arithmetics. See

12 Dec 18, 2022
Code for Multinomial Diffusion

Code for Multinomial Diffusion Abstract Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural ima

104 Jan 04, 2023
Get started with Machine Learning with Python - An introduction with Python programming examples

Machine Learning With Python Get started with Machine Learning with Python An engaging introduction to Machine Learning with Python TL;DR Download all

Learn Python with Rune 130 Jan 02, 2023
A self-supervised learning framework for audio-visual speech

AV-HuBERT (Audio-Visual Hidden Unit BERT) Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction Robust Self-Supervised A

Meta Research 431 Jan 07, 2023
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark (ICCV 2021)

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark (ICCV 2021) Kun Wang, Zhenyu Zhang, Zhiqiang Yan, X

kunwang 66 Nov 24, 2022
Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation.

PersonLab This is a Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation. The model predicts heatmaps and vari

OCTI 160 Dec 21, 2022
Weakly Supervised Learning of Rigid 3D Scene Flow

Weakly Supervised Learning of Rigid 3D Scene Flow This repository provides code and data to train and evaluate a weakly supervised method for rigid 3D

Zan Gojcic 124 Dec 27, 2022