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Malicious URL Detection is designed to identify potentially harmful URLs using machine learning techniques. The model leverages various features of URLs to classify them as malicious or benign, providing an essential prediction for cybersecurity.
Our project employs machine learning to pinpoint phishing URLs with 97.4% accuracy, leveraging HTTPS and website traffic as critical indicators. Insights into features like AnchorURL enhance cybersecurity strategies, showcasing the power of AI in combating online threats.
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 -
TrustLink: Detect and safeguard against deceptive URLs. Real-time threat detection using browser extension and web application for enhanced online security.
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.
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 …
Associated-Threat-Analyzer detects malicious IPv4 addresses and domain names associated with your web application using local malicious domain and IPv4 lists.