|
|
# flock-botnet
A twitter flock botnet with bots replying tweets with text generated by Markov chains
#### generating text with Markov chains
Markov chain: https://en.wikipedia.org/wiki/Markov_chain
The algorithm calculates the probabilities of Markov chains, analyzing a considerable amount of text, for the examples, I've done it with the book "The Critique of Pure Reason", by Immanuel Kant (http://www.gutenberg.org/cache/epub/4280/pg4280.txt).
### Replying tweets with Markov chains
When the botnet is up working, the bots start streaming all the twitter new tweets containing the configured keywords. Each bot takes a tweet, analyzes the containing words, and generates a reply using the Markov chains previously calculated, and posts the tweet as reply.
In the following examples, the bots ("andreimarkov", "dodecahedron", "projectNSA") are replying some people.
![flock-botnet](https://raw.githubusercontent.com/arnaucode/flock-botnet/master/screenshots/01.png "01")
-
![flock-botnet](https://raw.githubusercontent.com/arnaucode/flock-botnet/master/screenshots/02.jpeg "02")
-
![flock-botnet](https://raw.githubusercontent.com/arnaucode/flock-botnet/master/screenshots/03.jpeg "03")
-
![flock-botnet](https://raw.githubusercontent.com/arnaucode/flock-botnet/master/screenshots/04.jpeg "04")
configuration file example (flockConfig.json): ``` [{ "title": "account1", "consumer_key": "xxxxxxxxxxxxx", "consumer_secret": "xxxxxxxxxxxxx", "access_token_key": "xxxxxxxxxxxxx", "access_token_secret": "xxxxxxxxxxxxx" }, { "title": "account2", "consumer_key": "xxxxxxxxxxxxx", "consumer_secret": "xxxxxxxxxxxxx", "access_token_key": "xxxxxxxxxxxxx", "access_token_secret": "xxxxxxxxxxxxx" }, { "title": "account3", "consumer_key": "xxxxxxxxxxxxx", "consumer_secret": "xxxxxxxxxxxxx", "access_token_key": "xxxxxxxxxxxxx", "access_token_secret": "xxxxxxxxxxxxx" } ]
```
|