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k9team3's Projects

rbenv icon rbenv

Groom your app’s Ruby environment

searx icon searx

Privacy-respecting metasearch engine

selenium-friends-scraper icon selenium-friends-scraper

Python code that simulates the activity of crawling Facebook to get friends of friends network with using the API.

shadow icon shadow

Shadow is a unique discrete-event network simulator that runs real applications like Tor, and distributed systems of thousands of nodes on a single machine. Shadow combines the accuracy of emulation with the efficiency and control of simulation, achieving the best of both approaches.

smqtk icon smqtk

Python toolkit for pluggable algorithms and data structures for multimedia-based machine learning.

snapchat-decrypt icon snapchat-decrypt

Python script for decrypting stored images from Snapchat version 5.0.34.nn

social-network-graph icon social-network-graph

Provides a relational graph (nodes and edges) representing the relationships between facebook friends

sparkler icon sparkler

Spark-Crawler : Evolving Apache Nutch to run on Spark.

stroom icon stroom

Stroom is a highly scalable data storage, processing and analysis platform.

telegram_bot_logger icon telegram_bot_logger

A bot for Telegram that automatically logs all messages from a channel/group into a database

thesis-bitcoin-clustering icon thesis-bitcoin-clustering

The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.

tldextract icon tldextract

Accurately separate the TLD from the registered domain and subdomains of a URL, using the Public Suffix List.

wart icon wart

WhatsApp Registration Tool

waybackpack icon waybackpack

Download the entire Wayback Machine archive for a given URL.

web-crawlers icon web-crawlers

Created to scrape websites and LinkedIn pages of companies for certain keywords

whoishere.py icon whoishere.py

WIFI Client Detection - Identify people by assigning a name to a device performing a wireless probe request.

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