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Home Page: http://dn.institute/
License: The Unlicense
Distributed Networks Institute
Home Page: http://dn.institute/
License: The Unlicense
Right now images in the posts are not presented in the best way.
Problems to fix:
Improvements:
To Do:
This challenge aims to capture a wide range of contributions to the Crypto Attacks Wiki. To participate, submit a pull request that adds or modifies files in the attacks
directory and request a review from this issue assignees. All submissions will be reviewed by the wiki maintainers, and additional changes to your pull request may be asked of you to bring your submission to the quality level of the rest of the wiki.
Before committing to the wiki, please ensure your submission meets the following criteria:
Header name | Required | Description | Example |
---|---|---|---|
date |
yes | YYYY-MM-DD | 2012-07-16 |
target-entities |
yes | Entities that were targeted by the attackers. Multiple values allowed | Binance , Localbitcoins , Ethereum |
entity-types |
yes | General category describing targeted entity. Check existing ones in the examples and suggest yours if not present. Multiple values allowed | Custodian , DeFi , GameFi , Exchange , Wallet , Blockchain , Bridge , Yield Aggregator , Lending Platform , Stablecoin , Token , NFT |
attack-types |
yes | Common hacking technique, check existing ones in the examples and suggest yours if not present. Multiple values allowed | 51% , Wallet Hack ,Private Key Leak , Infrastructure Attack , Smart Contract Exploit , Flash Loan Attack , Phishing , Signature Verification Issue , Brute Force , Race Condition Exploit |
title |
yes | Article Title | BitGrail Hack Results in $170 Million Loss |
loss |
yes | Loss (In approximate USD equivalent at time of incident) | 50000 |
If the changes requested by reviewers are not addressed within a week, the PR will be considered stale and will be closed.
Our crowdsourced Crypto Attack Wiki has a number of bots in charge of automated quality checks. Things have changed since we first implemented the quality checks, and now most of the functionality is covered by GPT-like services. It should be possible to refactor existing quality checks to fit the whole pull request (instead of splitting it into chunks) into one API request. Anything that goes beyond LLM's context window, can safely be cut out and produce a warning to submit smaller PRs. Although OpenAI API still lacks web browsing functionality, there are alternative solutions offered by Goggle or Azure. Reach out if you need test API keys.
To participate in the challenge, submit a pull request that refactors existing bots into one GPT-like API request and request a review from this issue assignees. Below are some improvement ideas, but feel free to suggest other functionality.
All submissions will be reviewed by the wiki maintainers, and additional changes to your pull request may be asked of you to bring your submission to improve code quality, security, and/or efficiency.
Email your bitcoin or stablecoin payment address to [email protected]. Expect all payouts to be completed by the end of the month.
π @Kseymur manged to figure out most of the needed improvements using Claude and Brave API.
article-name.md
file with the article text to content/market-health
content/market-health/img/article-name
To participate, submit a pull request that adds a simple and lightweight widget for displaying Wash Trading and Front Running Metrics on the Market Health Metrics Documentation page located here, and add this issue assignee as a reviewer. Feel free to use whatever JS/TS libraries and frameworks, but keep in mind that your submissions will be evaluated based on the combination of cost/efficiency, security, and load speed. Below is a rough sketch of the widget position on the page, no need to make it look identical:
This time, we offer multiple bounties for the top 3 submissions:
π₯ $1000
π₯ $500
π₯ $250
Anyone can participate and submit their pull request before the deadline. We will also reach to all challenge participants with successful submissions to talk about more permanent research and development opportunities within our projects. Spread the word - additional $250 π° will be paid for referrals of any winning submissions!
See more details on the Challenge Program and check out the success stories of the challenge winners.
Looking forward to your submissions!
- What is the difference between
Security incidents
andAttacks
in the main outline? I would put all attack categories under one parent and make it clickable and leading to the full list
Originally posted by @marina-chibizova in #3 (comment)
title
stype
s (docs and posts). Mixing them in the menu is flaky.categories
are used for attack types and tags
- for other things like custodian names. Might be better worth creating to create separate fields for cleaner taxonomy. This could help with adding "recent posts" block to category articles later on (example).Front-running is trading stock or any other financial asset by a broker who has inside knowledge of a future transaction that is about to affect its price substantially. A broker may also front-run based on insider knowledge that their firm is about to issue a buy or sell recommendation to clients that will almost certainly affect the price of an asset.
In the crypto industry, front-running is a common occurrence, with even the biggest trading venues involved. What often starts as an in-house bot to provide additional liquidity to a centralized exchange, sometimes turns in an illegal tool for to earn money by trading ahead of their own customers.
Please comment below with ideas on detecting front-running trades based on a stream of executed trades. More specifically, describe an algorithm you would use to create a metric capable of automatically flagging suspicious trades. Feel free to support your ideas by adding references, datasets, graphs, and code. Comments with the best ideas will be hidden to allow others to participate. Multiple submission awards are available.
Many of the previous challenge participants focused on investigative approaches that involved manual analysis of specific cases of front-running. This, however, is an engineering challenge, requiring successful submissions to include an algorithm, supported by references, datasets, graphs, and/or code. We have more than enough of ingenious ideas on how it can be done, but no solid plans of how to implement it using real-time streaming data.
JS collapsible doesn't work on some devices, let's switch to the basic functionality of hugo-native collapsible and see when it's not enough and we need something else
The goal of this challenge is to create a scraper for Twitter home "for you" timeline. We are interested in capturing relevant to us (according to Twitter recommendation engine) tweets / liked tweets / retweets.
The scraper will be deployed as a scheduled function that periodically checks timeline updates and sends new tweets to a MongoDB collection. Avoid Twitter's official API, but feel free to leverage their front-end GraphQL API or other reliable ways of obtaining the data. Please keep in mind that the submissions will be evaluated based on the combination of cost/efficiency, security, and stability.
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"properties": {
"timestamp": {
"type": "number",
"description": "Unix epoch message timestamp"
},
"message": {
"type": "object",
"properties": {
"text": { "type": "string" },
"id": { "type": "string", "description": "Message ID" },
"language": {
"type": "string",
"description": "Language of the Message"
},
"enrichment": {
"$ref": "./enrichment_fields.json"
},
"media": {
"type": "array",
"items": {
"type": "object",
"properties": {
"type": { "type": "string", "enum": ["image", "audio", "video"] },
"id": { "type": "string" },
"title": { "type": "string" },
"file_name": { "type": "string" },
"url": {
"type": "string",
"description": "URL of the media file"
}
}
}
}
},
"required": ["text", "id"]
},
"user": {
"type": "object",
"properties": {
"name": { "type": "string" },
"id": { "type": "string" }
},
"description": "Sender of the Message"
},
"geo_coords": {
"$ref": "https://geojson.org/schema/GeoJSON.json"
},
"source": {
"type": "object",
"properties": {
"platform": {
"type": "string",
"description": "Telegram/Twitter/Reddit"
},
"channel": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Name of the message source, such as a Telegram channel"
},
"id": { "type": "string" }
}
},
"referenced_post": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "URL link of the referenced post"
},
"id": { "type": "string" }
}
},
"source_specific_fields": {
"type": "object",
"additionalProperties": true,
"description": "Extra source-specific fields"
}
},
"required": ["platform"]
}
},
"required": ["timestamp", "message", "user", "source"]
}
To participate, submit a pull request with your solution, along with a .json with sample data, and request a review from @jalmonter. Expanding the pull request description with your methodology can help us better understand your reasoning and evaluate your submission faster.
π @Hjklvfr submitted the winning pull request and claimed the bounty!
Improvements to reporter service
creating an issue for this
Misspelled: for **threat** modeling systems
Originally posted by @Lavriz in #3 (comment)
Some headers don't pulling - https://github.com/1712n/dn-institute/actions/runs/5205723830
Tool that creates automated weekly reports with metrics spikes and their interpretation.
Data source (wash trading metrics): https://rapidapi.com/DNInstitute/api/crypto-market-health/
Article examples https://dn.institute/market-health/posts/
Theory https://dn.institute/market-health/docs/market-health-metrics/
@jhirschkorn wants to incentivize creation of wiki pages that have certain tags/categories. There are a few approaches to this, here are just some implementation ideas:
payout
comment, i.e. payout\X2
or something.Whichever is easier to implement. Feel free to use your own approach if it works for @jhirschkorn.
Follow up #24
In a PR, the runtime context is the branch origin repository. This is a GH security policy and otherwise anyone could submit a malicious PR to leak secrets. So the workflow fails with PRs from forked repos because it will run in the wrong context without the GH read/write and the OpenAI key.
I have to study an alternative besides denying PRs from forked repos or adopting a self-hosted runner.
Requested by #203 (comment)
We invite you to enrich our Market Health wiki by identifying, analyzing, and documenting instances and methods of market manipulation, leveraging data and insights gained from various sources, including our free API that provides basic metrics related to wash trading activities. Comprehensive documentation on each metric is available here.
To participate, submit a pull request that either adds a new page or enhances existing pages in the market-health
directory and ask for a review by the issue assignees. Ensure that your submissions are thorough, data-backed, and adhere to the submission guidelines detailed below.
$500 for any accepted pull request with a single article. We'll prorate your submission up or down according to the pull request stats (20c/character). That said, our advice is to create smaller pull requests to speed up the review process and avoid any potential branch conflicts stemming from other people working on the same things in parallel. We will award additional bounties for fixing factual errors, suggesting accepted structural changes, and adding more advanced content to other parts of the wiki.
Email your bitcoin/lightning payment address to [email protected]. Expect all payouts to be completed by the end of the month.
New articles discovering instances of manipulation on cryptocurrency exchanges
Improve existing content by:
Ensure your submission complies with the following:
.md
file and all supporting images and datasets. Use clear naming convention for all your filesHeader name | Required | Description | Example |
---|---|---|---|
date |
Yes | YYYY-MM-DD | 2023-10-02 |
entities |
Yes | Entities implicated in wash trading. | Huobi ,HT ,TRX ,DOGE |
title |
Yes | Article Title | Uncovering Wash Trading and Market Manipulation on Huobi |
Problem 1:
Fix 1:
Ideally, we would want to have a carousel/gallery view in the cases of several images stacked, so they would look like in Instagram, and users could horizontally swipe through them.
requirements:
Problem 2:
Images inside text are not rendered anywhere in github (markdown, devmode or codespace), the only way to see them is after the page is added to the website
Fix 2:
Not sure how to deal with it, as far as I know current way of presenting pictures in the text was implemented to be able to make captions formattable (so they look like captions - centered, cursive, etc). This feature should not be lost
The goal of this challenge is to collect a minimum of 200 tweets related to crypto custodians and at least one of the topics below.
Topic | Sentiment | Notes |
---|---|---|
hacker attacks | negative | DDoS, hacks, stolen funds, etc. - anything that relates to hacker attacks and security breaches at crypto custodians. See examples of the hacker attacks and attack-related tweets. |
law enforcement | negative | anything that relates to law enforcement regarding projects in crypto, or their employees/board (custodians, tokens, protocols, etc): potential litigation, enforcement actions, court proceedings, etc. See examples of the law enforcement-related tweets |
uptime problems | negative | Anything that would affect crypto custodian availability: downtime of any sort, matching orders engine issues, freezing website, API lags, planned and unplanned maintenance, service outages, etc. See examples of the uptime-related tweets |
withdrawal issues | negative | Anything that prevents/slows transfers of the money out/in: withdrawals/deposits aren't possible, the fees aren't matching, the balance isn't updated, frozen wallets, prolonged system downtime and verification process, etc. See examples of the withdrawal-related tweets. |
fraud | negative | Anything that implies illegal activity happening at crypto custodians: exit scams, pump-and-dump schemes, front running, wash trading, etc. See examples of the fraud-related tweets. |
To participate, submit a pull request adding at least 200 tweets related to crypto custodians to at least one of the topic datasets and request a review from this issue assignee. Expanding the pull request description with your methodology can help us better understand your reasoning and evaluate your submission faster.
Some short prompts returning just point, and it can fixed
@danisztls, I think there is a problem with your payout-calc logic.
Originally posted by @evgenydmitriev in #76 (comment)
@JediFaust, here's the list of spellchecker fixes needed. Involve @danisztls and figure out LanguageTool paid account if needed.
To design a model capable of selecting the top 10% of predictions that will exhibit the smallest mean distance.
You have an option to work with either or both of the text data and raw predictions. Additionally, you may perform any form of aggregation or transformation on the raw predictions as you see fit.
The primary metric for this challenge is the mean distance of the selected top 10% predictions. Your objective is to minimize this value.
As a secondary metric, we designed the Class Representation Index (CRI). In essence, CRI compares the class distribution before and after filtering, giving a higher weight to classes that were initially larger. The primary purpose of this metric is to detect cases where a class is significantly less represented after filtering compared to its original size.
See comment.
Wash trading is a process whereby a trader buys and sells a security for the express purpose of feeding misleading information to the market. In some situations, wash trades are executed by a trader and a broker who are colluding with each other, and other times wash trades are executed by investors acting as both the buyer and the seller of the security.
In the crypto industry, wash trading is a common occurrence, as many traders equate high trading volumes to healthy market liquidity. Trading venues and crypto projects are complacent and often collude with each other to generate fake trades that cost little to place and execute. You can find more technical details on wash trading techniques in this YouTube video.
Please comment below with ideas on detecting wash trading based on a stream of executed trades. More specifically, describe an algorithm you would use to create a metric capable of measuring wash trading in a given market pair volume. Feel free to support your ideas by adding references, datasets, graphs, and code. Comments with the best ideas will be hidden to allow others to participate. Multiple submission awards are available.
Many of the previous challenge participants focused on investigative approaches that involved manual analysis of specific cases of wash trading. This, however, is an engineering challenge, requiring successful submissions to include an algorithm, supported by references, datasets, graphs, and/or code. We have more than enough of ingenious ideas on how it can be done, but no solid plans of how to implement it using real-time streaming data.
@danisztls , is there a way we can automate the counting of characters and calculation of costs for submissions after they are approved?
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Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
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