Fake-user-agent-traffic-geneator - Python CLI Tool to generate fake traffic against URLs with configurable user-agents

Overview

Fake traffic generator for Gartner Demo

Generate fake traffic to URLs with custom user-agents

Usage

Running Complete

Tested with Python 3.9.5 and pip 22.0.3 on MacOS 10.15.7

I highly recommend you always run each Python project within its own virtualenv. The commands below assume you have already created and activated a virtualenv for this project.

git clone [email protected]:newrelic-experimental/fake-user-agent-traffic-geneator.git
cd fake-user-agent-traffic-geneator
pip install -r requirements.txt
python generate.py

Config

Configuration is done via the config.toml file.

Global settings

Name Type Description
concurrency int max asyncio primitives
urls list[str] List of URLs to target

Target settings

The target of each request is grouped together into Targets

Name Type Description
allowed_request_types list[str] Allowed request types
url str The URL to request
form Optional[Dict[str, Dict[str, str]]] Form submission details for request (browser only)
form.button_selector str CSS selector of the form submit button
form.inputs.selector str CSS selector for form input field
form.inputs.value str Value to enter into form input field

Request settings

Request specific settings are grouped together into Personas, you can create as many personas as you would like for each run.

Name Type Description
request_type "browser" or "api" How the request should be executed ("browser" is required for RUM)
min_requests int Minimum number of requests to make per URL
max_requests int Maximum number of requests to make per URL
timeout Optional[int] Request timeout in seconds
cache_enabled Optional[bool] Enable browser cache (only used when browser=true)
user_agents list[str] User-Agent strings to use. A random ua will be choosen per request
custom_headers Optional[list[list[str, str]]] Any other headers to send with the request. See the example below for syntax
color str Persona text color in progress bar

Each custom header must be a list where index 0 is the header key and index 1 is the header value. For example:

custom_headers = [["X-Script-Version", "v0.0.1"], ["X-Something-else", "abc"]]

Owner
New Relic Experimental
Experimental code and projects by @newrelic employees (Relics) and our community members across the globe.
New Relic Experimental
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

MIND 478 Jan 01, 2023
基于YoloX目标检测+DeepSort算法实现多目标追踪Baseline

项目简介: 使用YOLOX+Deepsort实现车辆行人追踪和计数,代码封装成一个Detector类,更容易嵌入到自己的项目中。 代码地址(欢迎star): https://github.com/Sharpiless/yolox-deepsort/ 最终效果: 运行demo: python demo

114 Dec 30, 2022
Chainer Implementation of Semantic Segmentation using Adversarial Networks

Semantic Segmentation using Adversarial Networks Requirements Chainer (1.23.0) Differences Use of FCN-VGG16 instead of Dilated8 as Segmentor. Caution

Taiki Oyama 99 Jun 28, 2022
This is the workbook I created while I was studying for the Qiskit Associate Developer exam. I hope this becomes useful to others as it was for me :)

A Workbook for the Qiskit Developer Certification Exam Hello everyone! This is Bartu, a fellow Qiskitter. I have recently taken the Certification exam

Bartu Bisgin 66 Dec 10, 2022
Codes for "Template-free Prompt Tuning for Few-shot NER".

EntLM The source codes for EntLM. Dependencies: Cuda 10.1, python 3.6.5 To install the required packages by following commands: $ pip3 install -r requ

77 Dec 27, 2022
Exploiting a Zoo of Checkpoints for Unseen Tasks

Exploiting a Zoo of Checkpoints for Unseen Tasks This repo includes code to reproduce all results in the above Neurips paper, authored by Jiaji Huang,

Baidu Research 8 Sep 06, 2022
Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models

merged_depth runs (1) AdaBins, (2) DiverseDepth, (3) MiDaS, (4) SGDepth, and (5) Monodepth2, and calculates a weighted-average per-pixel absolute dept

Pranav 39 Nov 21, 2022
Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection

CP-Cluster Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection, Instance Segme

Yichun Shen 41 Dec 08, 2022
[NeurIPS'21] Projected GANs Converge Faster

[Project] [PDF] [Supplementary] [Talk] This repository contains the code for our NeurIPS 2021 paper "Projected GANs Converge Faster" by Axel Sauer, Ka

798 Jan 04, 2023
Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures.

NLP_0-project Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures1. We are a "democratic" and c

3 Mar 16, 2022
《Improving Unsupervised Image Clustering With Robust Learning》(2020)

Improving Unsupervised Image Clustering With Robust Learning This repo is the PyTorch codes for "Improving Unsupervised Image Clustering With Robust L

Sungwon Park 129 Dec 27, 2022
A package to predict protein inter-residue geometries from sequence data

trRosetta This package is a part of trRosetta protein structure prediction protocol developed in: Improved protein structure prediction using predicte

Ivan Anishchenko 185 Jan 07, 2023
Code accompanying our paper Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in "Feature Learning in Infinite-width Neural Networks" This repo contains code to replicate our experiments (Word2Vec, MAML) in

Edward Hu 37 Dec 14, 2022
N-RPG - Novel role playing game da turfu

N-RPG Ce README sera la page de garde du projet. Contenu Il contiendra la présen

4 Mar 15, 2022
Implementation of paper "Graph Condensation for Graph Neural Networks"

GCond A PyTorch implementation of paper "Graph Condensation for Graph Neural Networks" Code will be released soon. Stay tuned :) Abstract We propose a

Wei Jin 66 Dec 04, 2022
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

EFENet EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation Code is a bit messy now. I woud clean up soon. For training the EF

Yaping Zhao 19 Nov 05, 2022
Must-read Papers on Physics-Informed Neural Networks.

PINNpapers Contributed by IDRL lab. Introduction Physics-Informed Neural Network (PINN) has achieved great success in scientific computing since 2017.

IDRL 330 Jan 07, 2023
A geometric deep learning pipeline for predicting protein interface contacts.

A geometric deep learning pipeline for predicting protein interface contacts.

44 Dec 30, 2022
Code for the AI lab course 2021/2022 of the University of Verona

AI-Lab Code for the AI lab course 2021/2022 of the University of Verona Set-Up the environment for the curse Download Anaconda for your System. Instal

Davide Corsi 5 Oct 19, 2022