Machine learning to classify Malicious (Spam)/Benign URL's

Offensive & proactive tool designed to disrupt phishing attacks by flooding fake phishing websites' login portals with a deluge of fake user data, sometimes even takes down their entire site in the process.

Extracting features from URLs to build a data set for machine learning. The purpose is to find a machine learning model to predict phishing URLs, which are targeted to the Brazilian population.

Custom SpamAssassin rules I and others have made and contributed with - To mitigate spam mails and phishing mails now also with cool Phishtank rules

Measurement system I built during my PhD to collect and analyse large-scale datasets; including phishing and malware attacks on Twitter, blacklist characterisation, and phishing detection capabilities of web browsers.

gaw.sh url shortener

🐟🐠🐡🎣 Validate 'Phishing' 'URLs' with the 'PhishTank' Service

Phishing URL Dataset collected from IP2Loaction and PhishTank

Simple web API using the hourly dump from Phishtank

Small utility that loads any downloaded JSON databases from www.phishtank.com into Redis cache for quick local queries

A simple phishtank.com API gem

Get the kind of content hosted by a domain based on the domain name

A simple PHP wrapper for the PhishTank API.

PhishTank Memcached

Rules that check if the page or linked pages on the same domain (or external) contain any unwanted software,malware or reported phishing attacks

Search PhishTank for a URL and return its status.

Python API for Phishtank Lookup

The server side of PB Project.

PhishFinder - Detecting Phishing Websites using Machine Learning on IBM Cloud