A "decentralized exchange" is a currency exchange which lives and is run completely as a smart contract on the blockchain with no central authority or party running the backend. Funds are held in a smart contract and secured with a public/private key pair, such that each buy/sell/withdraw can only be invoked by the wallet owner and not by the central cluster admin. The smart contract itself is run on the Ethereum Virtual Machine, which is comprised of hundreds of thousands of nodes that run independently on people's personal computers (and GPU farms!) but store every event on a public ledger. This enables a powerful platform for Investors, but also for money launderers, and "pump and dump" schemers. For this demo, we will use popular data science tools to analyze EtherDelta's books—a cryptocurrency exchange with over 1 billion USD worth of funds in the "smart contract"—and leverage this publicly available dataset to expose which "coin" may be associated with scams, as they happen. From a technology stack, we will showcase how events on a blockchain can be analyzed in modern big data architectures. These events could be the logs of a smart contract execution, for which we'll show how to leverage Spark via a Jupyter or Zeppelin Notebook to perform ETL using the power of a remote Hadoop cluster. This will cover our experiences in the slowness and limitations of querying data directly from the blockchain, and how a Kafka producer/consumer model works well for analyzing granular level applications running on one of the many blockchains/tangles arising in the crypto-currency decentralized compute world. KAT PETRE, Ambari Product Owner, Hortonworks and JESUS ALVAREZ Advisory Software Engineer, IBM

1. Detecting Real-Time Market
Manipulation
Across decentralized cryptocurrency exchanges
Jesus Alvarez, IBM
Kat Petre, Hortonworks
06/3/2018

2. Agenda
❖ What this talk is (and what it’s not)
❖ Approach Evolution
❖ Blockchain Smart contracts 101
❖ Anatomy of Volume Based Manipulation
❖ Interacting with data off of the blockchain
❖ Demo Lab Environment
❖ ShitCoin Detector Architecture
❖ ShitCoin Detector Demo
❖ ShitCoin of the Month

3. Approach Evolution
2013
Mining
2014
Arbitraje
2015
Trading bot
2015
Litora
Lorem ipsum dolor
sit amet,
consectetur
adipiscing.
MAY
Quis Diam
Lorem ipsum dolor
sit amet,
consectetur
adipiscing.
JUN
Dolor Lorem
2016 20182017
Hodl + manual Bot Feed
Automation ,
Analytics,
(& Tax Bills)

4. Blockchain 1.0
Sending bitcoins from one party to another, with no central bank
“Miners” verify transactions using specialized hardware
Transactions stored on Bitcoin’s public distributed ledger
Block #12345
{
From: bob
To: charlie
Amount: 1BTC
…
},
{From: address:foo
To: ..
Block #12346
{
From: address:..
To: address:..
Amount:
…
},
{From: address:foo
To: ..
Logs on the blockchain

5. Blockchain 2.0 - Smart Contracts
Example: Crypto-Casino
Block #12345
{ From: bob,
To: exchange,
InputData: {
Buy: tkn,
Amount: 1
… },
{From: address:foo
To: ..
Block #12346
{ From: alice,
To: casino,
InputData: {
Roll: 51,
Bet: 2eth,
… },
{From: address:foo
To: ..
Example: Currency Exchange
Logs on the blockchain

6. The Anatomy of a Volume Based Market
Manipulation
I want to:
BUY 120M SHTs
For a total of :
24.324ETH ( ~$13k USD)
I’ll take that exact order!:
SELL 120M SHTs
For a total of:
24.324 ETH - 0.25% SHT
(Exchange fees paid in SHT)
Charlie
(Creator of SHTCOIN)
Also, Charlie
➔ SHT Reaches Highest Volume on
exchange
➔ New investors believe this “cheap” coin has momentum, and buy into it

7. Asking the right questions
- Look for transactions whose (buyer == seller)
web3.eth.getBlock()
{
author: miner,
difficulty: int,
transactions: [
0x..101,
0x..aa,
0x..],
web3.eth.getTransactionRe
ceipt(0x..101)
{
blockNumber: int,
contractAddress: 0x..,
Logs : {
topics: [ HexBytes('0x..d’),
HexBytes('0x..a’)],
data: “0x.. 640 hex
characters”,
logIndex: ...
contractABI.method.processReceipt(0x.101)
{'blockNumber': 5681296, 'transactionIndex':
36, 'logIndex': 30,
'event': 'Trade',
'address': '0x',
'Args': {
‘Get’ : ‘0x buyer address’,
‘AmountGet’: 1000,
‘TokenGet’ : “token address”,
‘Give’: ‘0x seller address’,
‘AmountGive’: 1,
‘TokenGive” : “token address }
Blocks
Transactions
Contract Execution Logs

8. Digital Ocean Lab Environment
Total Cost = $430/month
2 x 2GB RAM = $30/month
4 x 8GB RAM = $160/month
3 x 16GB RAM = $240/month

9. ShitCoin Detector Architecture

10. ShitCoin Detector in Practice (demo)

11. Shitcoin of the Month
Search the top shitcoin in etherscan: https://etherscan.io/token/0x21692a811335301907ecd6343743791802ba7cfd
and retrace their scammer’s steps:
Step 2. Created enough fake volume to give ADU
a price of 0.000003 ETH/ADU, by creating trade orders
for 16.7M ADU/50.1ETH
Step 1. Created aducoin with 110 billion tokens
https://forkdelta.github.io/#!/trade/ADU-ETH

12. So how much $$ are we talking about here?
Step 4. Trick 100+ new investors to buy
ADU, while maintaining the artificial
price.
Step 3. Those ADU’s are now “worth” $11.7M USD at the
current rates.
Step 5. Sell & Profit

13. Questions
- Blockchain demos open source
- https://github.com/blockchain-
demos/DWSummit2018