A secure low code deception runtime framework, leveraging AI for System Virtualization.
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Updated
Nov 3, 2026 - Go
A secure low code deception runtime framework, leveraging AI for System Virtualization.
A simple server to act as a Veeam "honeypot" providing alerting for network scans for Veeam services
dataset of short peptides (3-7 amino acids) bound to proteins annotated for structural similarity and augmented with docking decoys
LLM-based Honeypot for context-aware honeypots
Multi-step pipeline that provides tools for filtering ChEMBL protein target using both physical and chemical features. This project was created in terms of my masters thesis which can be read here: https://www.overleaf.com/read/kwmnwnrwkntb
A pre-registered negative result from structure-based pharmacophore screening — plus four reusable validation checks: ratio-aware Güner–Henry, property-only decoy baseline, benchmark overlap, and a feature-presence prefilter that predicts failure before you dock.
Approximate palindromes lead to overstated false match rates in benchmarks using reversed sequences
A curated knowledge base of dummy targets and decoys across physical, signal, cyber, historical, and autonomous deception systems.
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