An adaptive AI honeypot system with attacker behavioral profiling and reinforcement learning capabilities

AI-Driven Adaptive Deception & High-Interaction Honeypot System (AEGIS MK_II) cybersecurity, honeypot, ai-security, purple-teaming, active-defense

AI-powered adaptive honeypot system that simulates vulnerable services to capture, analyze, and classify advanced cyber threats, including automated and AI-driven attacks, through real-time behavioral monitoring and intelligent pattern analysis.

A real-time AI-powered fraud detection system leveraging Graph Neural Networks (GNNs), Transformers, and blockchain to identify and prevent fraudulent transactions. Features adaptive machine learning models, real-time risk scoring, AI honeypots, and federated learning for secure, scalable fraud detection.

AegisGuard is an intelligent network security system that uses multi-agent reinforcement learning to autonomously defend networks. Unlike static firewalls, the system deploys specialized AI agents that collaborate in real-time to detect threats, dynamically reconfigure security protocols, quarantine malicious nodes and deploy adaptive honeypots.

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Built an SDK-based adaptive cyber defense system using AI-driven deception and honeypots to handle malicious inputs instead of blocking.

Real-time, multi-attacker deceptive cyber defense system using AI/ML to detect, deceive, and manipulate attackers with intelligent honeypots and adaptive responses.

An AI-powered agentic honeypot that detects and engages with scam attempts by simulating realistic human personas. It intelligently prolongs conversations to extract valuable threat intelligence such as UPI IDs, phone numbers, and phishing links. The system uses LLM-driven reasoning to identify scams and generate adaptive responses in real time.

Dynamic Honeypot Network with Adaptive Deception Techniques A honeypot system that dynamically changes its behavior, appearance, and vulnerabilities based on attacker tactics to gather richer intelligence. Uses AI to simulate realistic environments and adapt in real-time.

Adaptive AI-Driven Network Defense System monitors network traffic using machine learning to detect evolving cyber threats. It automatically blocks attacks, redirects attackers to honeypots, and provides real-time security alerts.

Neural-IoT: A Generative Adaptive Deception Framework An advanced high-interaction honeypot system that utilizes Reinforcement Learning (RL) to optimize defense strategies and Generative AI (LLMs) to fabricate unique bait files in real-time.

Honeypot_in_AI is a security-focused project that uses AI to detect, analyze, and respond to malicious behavior by mimicking vulnerable systems. It leverages machine learning to identify attack patterns, gather threat intelligence, and improve defensive strategies through adaptive, intelligent deception.

Securex is an adaptive honeypot system that detects, deceives, and analyzes cyber attackers. Using progressive deception, AI threat classification, and secure blockchain-inspired logging, it engages threats to gather intelligence and waste attacker resources rather than immediately blocking them.