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Cowrie SSH/Telnet Honeypot https://docs.cowrie.org/
A secure low code honeypot framework, leveraging AI for System Virtualization.
安全、快捷、高交互、企业级的蜜罐管理系统，护网；支持多种协议蜜罐、蜜签、诱饵等功能。A safe, fast, highly interactive and enterprise level honeypot management system, supports multiple protocol honeypots, honeytokens, baits and other functions.
HellPot is a cross-platform portal to endless suffering meant to punish unruly HTTP bots.
honeyλ - a simple, serverless application designed to create and monitor fake HTTP endpoints (i.e. URL honeytokens) automatically, on top of AWS Lambda and Amazon API Gateway
A web honeypot library to create vulnerable-looking endpoints to detect and mislead attackers
Deception based detection techniques mapped to the MITRE’s ATT&CK framework
A PoC tool designed to enhance the effectiveness of your traps by spreading breadcrumbs & honeytokens across your systems to lure the attacker toward your honeypots
A PowerShell module to deploy active directory decoy objects.
Fingerprint-aware TLS reverse proxy. Use Finch to outsmart bad traffic—collect client fingerprints (JA3, JA4 +QUIC, JA4H, HTTP/2) and act on them: block, reroute, tarpit, or deceive in real time.
Add a layer of active defense to your cloud applications.
Voice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
