Honeybrid
Honeybrid is a network application built to deploy and administrate honeynets, providing the hybrid functionality of combining low and high interaction honeypots. It uses a Decision Engine to filter incoming traffic based on multiple criteria, a Control Engine to limit outgoing traffic, and a Redirection Engine to transparently redirect live network sessions. For more information about honeypots and honeynets, please refer to Niels Provos' honeyd website or Lance Spitzner's paper. Honeybrid has been sponsored by the Google Summer of Code 2009 and the Honeynet Project. Download the latest version of Honeybrid, beta-0.1.5, from SourceForge.
FEATURES
ALTERNATIVES
An SDN honeypot tool for detecting and analyzing malicious activities in Software-Defined Networking environments.
A honeypot daemon project for processing, filtering, and redirecting incoming traffic to a sandbox environment.
Distributed low interaction honeypot with Agent/Master design supporting various protocol handlers.
PINNED
InfoSecHired
An AI-powered career platform that automates the creation of cybersecurity job application materials and provides company-specific insights for job seekers.
Fabric Platform by BlackStork
Fabric Platform is a cybersecurity reporting solution that automates and standardizes report generation, offering a private-cloud platform, open-source tools, and community-supported templates.
Mandos Brief Newsletter
Stay ahead in cybersecurity. Get the week's top cybersecurity news and insights in 8 minutes or less.
Wiz
Wiz Cloud Security Platform is a cloud-native security platform that enables security, dev, and devops to work together in a self-service model, detecting and preventing cloud security threats in real-time.
RoboShadow
A cybersecurity platform that offers vulnerability scanning, Windows Defender and 3rd party AV management, and MFA compliance reporting, among other features.
Adversa AI
Adversa AI is a cybersecurity company that provides solutions for securing and hardening machine learning, artificial intelligence, and large language models against adversarial attacks, privacy issues, and safety incidents across various industries.