Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

Overview

dcf-game-infrastructure

All the components necessary to run a game of the OOO DC CTF finals.

Authors: adamd, hacopo, Erik Trickel, Zardus, and bboe

Design Philosophy

This repo contains all the game components necessary to run an Attack-Defense CTF that OOO used from 2018--2021.

The design is based on adamd's experience building the ictf-framework.

There are fundamental tenenats that we try to follow in the design of the system:

Spoke component model

The communication design of the components in the system (which you can kind of think of as micro-services) is a "spoke" model, where every component talks to the database (through a RESTish API), and no component directly talks to any other.

In this way, each component can be updated separately and can also be scaled independently using our k8s hosting.

This also made testing of each component easier, as the only dependence on a component is on the state of the database.

The only exception to this is the patchbot (the component that needs to test the patches submitted by the teams).

The database API puts the patchbot testing jobs into an RQ (Redis Queue), which all the patchbot workers pull jobs from.

Append-only database design

Fundamentally, a CTF database needs to calculate scores (that's essentially what the teams care about).

Prior design approaches that we've used would have a points or score column in the team table, and when they acquired or lost points, the app code would change this value.

However, many crazy things can happen during a CTF: recalculating scores or missed flags, even changing the scoring functions itself.

These can be difficult to handle depending on how the system is developed.

Therefore, we created a completely append-only database model, where no data in the DB is ever deleted or changed.

Even things like service status (the GOOD, OK, LOW, BAD that we used) is not a column in the services table. Every change of status would created a new StatusIndicator row, and the services would pull the latest version from this table.

Event model

Related to the append-only database design, everything in the database was represented by events.

The database would store all game events (in our game over the years was SLA_SCRIPT, FLAG_STOLEN, SET_FLAG, KOH_SCORE_FETCH, KOH_RANKING, PCAP_CREATED, PCAP_RELEASED, and STEALTH).

Then, the state of the game is based on these events.

An additional benefit is that these events could be shipped to the teams as part of the game_state.json.

Separate k8s clusters

How we ran this is with two k8s clusters: an admin cluster and a game cluster.

The admin cluster ran all of these components.

The game cluster ran all of the CTF challenges.

We used this design to do things like drop flags on the services. The flagbot used kubectl to drop a flag onto a service running in the other cluster.

This also allowed us to lock down the game cluster so that the vulnerable services couldn't make external requests, could be scaled separately, etc.

Install Requirements

This package is pip installable, and installs all dependencies. Do the following in a virtualenv:

$ pip install -e .

NOTE: If you want to connect to a mysql server (such as in prod or when deving against a mysql server), install the mysqlclient dependency like so:

$ pip install -e .[mysql]

Testing

Make sure the tests pass before you commit, and add new test cases in test for new features.

Note the database API now checks that the timezone is in UTC, so you'll need to specify that to run the tests:

$ TZ=UTC nosetests -v

Local Dev

If you're using tmux, I created a script local_dev.sh that will run a database-api, database-api frontend, team-interface backend, team-interface frontend, gamebot, and an ipython session with a database client created.

Just run the following

$ ./local_dev.sh

Deploy to prod

Build and -p push the image to production registry.

$ ./deploy.sh -p

Won't -r restart the running services, need to do:

$ ./deploy.sh -p -r

database-api

This has the tables for the database, a REST API to access it, and a python client to access the REST API.

See ooogame/database for details.

flagbot

Responsible for putting new flags into all the services for every game tick.

See ooogame/flagbot for details.

fresh-flagbot

Responsible for putting a new flags into a pod when it first comes up (from a team patching the service).

See ooogame/fresh_flagbot for details.

gamebot

Responsible for incrementing the game's ticks.

See ooogame/gamebot for details.

koh-scorebot

Responsible for extracting the King of the Hill (koh) scores from all the koh pods every tick, and submitting them to the database.

See ooogame/koh_scorebot for details.

team-interface

Responsible for providing an interface to the teams so that they can submit flags, get pcaps, upload patches, and get their patch status. Split into a backend flask REST API, which essentially wraps the database-api, and a React frontend.

See ooogame/team_interface for details.

pcapbot

Responsible for picking up all the newly generated pcaps, anonymize them, and if the service is releasing pcaps then release them.

See ooogame/pcapbot for details.

gamestatebot

Responsible for creating the game state at every new tick and storing them in the nfs, and release them publicly.

See ooogame/gamestatebot for details.

This is also the component that pushes data to the public scoreboard

Owner
Order of the Overflow
Order of the Overflow
Python interface for SmartRF Sniffer 2 Firmware

#TI SmartRF Packet Sniffer 2 Python Interface TI Makes available a nice packet sniffer firmware, which interfaces to Wireshark. You can see this proje

Colin O'Flynn 3 May 18, 2021
CMT: Convolutional Neural Networks Meet Vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers [arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no refer

FlyEgle 83 Dec 30, 2022
Official implementation of the Neurips 2021 paper Searching Parameterized AP Loss for Object Detection.

Parameterized AP Loss By Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, Jifeng Dai This is the official implementation of the Neurips 2021

46 Jul 06, 2022
cisip-FIRe - Fast Image Retrieval

Fast Image Retrieval (FIRe) is an open source image retrieval project release by Center of Image and Signal Processing Lab (CISiP Lab), Universiti Malaya. This project implements most of the major bi

CISiP Lab 39 Nov 25, 2022
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking We revisit and address issues with Oxford 5k and Paris 6k image retrieval benchm

Filip Radenovic 188 Dec 17, 2022
TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments for IV 2022.

TorchGRL TorchGRL is the source code for our paper Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffi

XXQQ 42 Dec 09, 2022
Pytorch implementation of Zero-DCE++

Zero-DCE++ You can find more details here: https://li-chongyi.github.io/Proj_Zero-DCE++.html. You can find the details of our CVPR version: https://li

Chongyi Li 157 Dec 23, 2022
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

Molecular Docking integrated in Jupyter Notebooks Description | Citation | Installation | Examples | Limitations | License Table of content Descriptio

Angel J. Ruiz Moreno 173 Dec 25, 2022
Fuse radar and camera for detection

SAF-FCOS: Spatial Attention Fusion for Obstacle Detection using MmWave Radar and Vision Sensor This project hosts the code for implementing the SAF-FC

ChangShuo 18 Jan 01, 2023
Code release for ICCV 2021 paper "Anticipative Video Transformer"

Anticipative Video Transformer Ranked first in the Action Anticipation task of the CVPR 2021 EPIC-Kitchens Challenge! (entry: AVT-FB-UT) [project page

Facebook Research 123 Dec 13, 2022
Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

Varun Nair 37 Dec 30, 2022
Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021

Learning Generative Models of Textured 3D Meshes from Real-World Images This is the reference implementation of "Learning Generative Models of Texture

Dario Pavllo 115 Jan 07, 2023
Unet network with mean teacher for altrasound image segmentation

Unet network with mean teacher for altrasound image segmentation

5 Nov 21, 2022
🍷 Gracefully claim weekly free games and monthly content from Epic Store.

EPIC 免费人 🚀 优雅地领取 Epic 免费游戏 Introduction 👋 Epic AwesomeGamer 帮助玩家优雅地领取 Epic 免费游戏。 使用 「Epic免费人」可以实现如下需求: get:搬空游戏商店,获取所有常驻免费游戏与免费附加内容; claim:领取周免游戏及其免

571 Dec 28, 2022
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
Flower - A Friendly Federated Learning Framework

Flower - A Friendly Federated Learning Framework Flower (flwr) is a framework for building federated learning systems. The design of Flower is based o

Adap 1.8k Jan 01, 2023
Randomized Correspondence Algorithm for Structural Image Editing

===================================== README: Inpainting based PatchMatch ===================================== @Author: Younesse ANDAM @Conta

Younesse 116 Dec 24, 2022
An self sufficient AI that crawls the web to learn how to generate art from keywords

Roxx-IO - The Smart Artist AI! TO DO / IDEAS Implement Web-Scraping Functionality Figure out a less annoying (and an off button for it) text to speech

Tatz 5 Mar 21, 2022
Pytorch Geometric Tutorials

Pytorch Geometric Tutorials

Antonio Longa 648 Jan 08, 2023
Fuzzing tool (TFuzz): a fuzzing tool based on program transformation

T-Fuzz T-Fuzz consists of 2 components: Fuzzing tool (TFuzz): a fuzzing tool based on program transformation Crash Analyzer (CrashAnalyzer): a tool th

HexHive 244 Nov 09, 2022