TrustShield AI: Hybrid ML-Based Phishing Detection using Flask, scikit-learn & M

<h1> Three Detection Paradigms. One Dataset. One Result. </h1> <p>For the last 147 days I’ve been building <a href="https://github.com/alonsoir/argus?utm_source=chatgpt.com"

<p>My mother called me last week. Someone had sent her an SMS <br /> claiming to be from DHL, asking her to pay a £2.99 customs <br /> fee via a link. She almost clicked it.</p>

<p>ML to blocklists: the next five years of the arms race between fraud and detection and what the arms race really looks like.<br /> A text file was the most dominant method of

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<p>Financial fraud in the modern digital age is complex, fast, and highly networked. Traditional relational (SQL) databases struggle when analyzing multi-hop connections—such as

<h2> Introduction </h2> <p>"Your account has been suspended." "Please update your payment information." — Phishing SMS and emails like these have become a daily occurrence in

<h1> Static Analysis for LLM Prompt Security: A Methodology for Pre-Deploy Vulnerability Detection </h1> <p><em>How applying SAST principles to LLM prompt strings catches security

<p><strong>Team Members</strong></p> <p>This project was developed by:<br /> <a class="mentioned-user" href="https://dev.to/lasyareddipatlolla">@lasyareddipatlolla</a> -PATLOLLA