Phishers use the websites which are visually and semantically similar to those real websites. So, we develop this website to come to know user whether the URL is phishing or not before using it. URL - http://phishing-url-detector-api.herokuapp.com/

Chrome extension for detecting phishing web sites

ShotDroid is a pentesting tool for android. There are 3 tools that have their respective functions, Get files from Android directory, internal and external storage, Android Keylogger + Reverse Shell and Take a webcam shot of the face from the front camera of the phone and PC.

NLP model and tech for cyber security tasks

Anomaly based Malware Detection using Machine Learning (PE and URL)

detect malicious URL and Request (Bi-LSTM、Bi-LSTM + CNN、CNN + Bi-LSTM、CNN + Bi-LSTM + CNN)

Detecting malicious URLs using Machine Learning

Malicious actors often reuse code to deploy their malware, phishing website or CNC server. As a result, similiaries can be found on URLs path by inspecting internet traffic. Moreover, deep learning models or even regular ML model do not fit for inline deployment in terms of running performance. However, regexes ( or YARA rules ) can be deployed …

Finds related domains and IPv4 addresses to do threat intelligence after Indicator-Intelligence collects static files.

人工智能检测恶意URL

Phishing Detection System using Natural Language Processing and Machine Learning

In this project, we have detected the malicious URLs using lexical features and boosted machine learning algorithms

Jupyter notebook for detecting malicious URLs

Associated-Threat-Analyzer detects malicious IPv4 addresses and domain names associated with your web application using local malicious domain and IPv4 lists.

hosts file to protect against malicious domains

Phishers use the websites which are visually similar to those real websites. So, we developed this website so that user can know whether the URL is phishing or not before using it. URL -

Implementation of `Detecting Malicious Websites by Learning IP Address Features` by Chiba et al. (2012)

Using machine learning to detect malicious urls