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IoT Security Gate with RFID, Real-Time Dashboard & ML Anomaly Detection | ISTE730 IoT Project
🔐 Automated Logistics and Access Security Gate
with Monitoring and Behavioral Analysis
👤 ABOUT ME:
Hi, I'm Vetrivel Maheswaran — currently pursuing my Master of Science in Information Technology
and Analytics at Rochester Institute of Technology (RIT), Rochester, New York.
This project was built as part of my coursework in ISTE730 - Foundations of IoT, where I combined
my interest in embedded systems, backend engineering, and machine learning into one complete working prototype.
Feel free to connect or reach out: 📧 vm6923@rit.edu | vetrim2003@gmail.com
📌 WHAT THIS PROJECT DOES:
This project implements a complete IoT-based smart security gate system using RFID authentication, real-time monitoring dashboard, and hybrid anomaly detection combining rule-based logic and Isolation Forest machine learning.
🔧 HARDWARE USED:
- Arduino UNO R3
- RC522 RFID Reader (13.56 MHz)
- Grove Servo Motor
- Grove LEDs (Red, Green, Blue)
- Grove Buzzer
- Grove LCD RGB Backlight v4.0
- Grove Base Shield
💻 SOFTWARE STACK:
- Arduino C/C++ — Edge firmware
- Python 3 — Backend
- FastAPI — REST API
- SQLite + SQLAlchemy ORM — Database
- PySerial — Serial communication
- Scikit-learn — Isolation Forest ML
- Chart.js — Dashboard visualization
⚙️ KEY FEATURES:
✅ RFID card authentication at the edge
✅ IN/OUT toggle tracking per user
✅ Real-time web dashboard (auto-refresh 5s)
✅ Today's stats — resets at midnight
✅ User presence tracking (Inside/Outside)
✅ Date and time range log filtering
✅ Per-user behavioral analytics page
✅ Anomaly detection — 3 rule-based checks
✅ Isolation Forest ML — unusual pattern detection
✅ Fully local — no cloud dependency
🚨 ANOMALY DETECTION:
Rule 1 → Off-hours access detection
Rule 2 → Rapid scan detection (sliding window)
Rule 3 → Consecutive denied streak detection
ML → Isolation Forest (unsupervised learning)
Feature: hour of day per user
Activates after 10+ scan history
📁 GITHUB REPOSITORY:
https://github.com/Vetrivel07/Automated-Logistics-and-Access-Security-Gate-with-Monitoring-and-Behavioral-Analysis
📚 COURSE:
ISTE730 — Foundations of IoT | Spring 2026
Rochester Institute of Technology (RIT)
Видео IoT Security Gate with RFID, Real-Time Dashboard & ML Anomaly Detection | ISTE730 IoT Project канала Vetrivel Maheswaran
with Monitoring and Behavioral Analysis
👤 ABOUT ME:
Hi, I'm Vetrivel Maheswaran — currently pursuing my Master of Science in Information Technology
and Analytics at Rochester Institute of Technology (RIT), Rochester, New York.
This project was built as part of my coursework in ISTE730 - Foundations of IoT, where I combined
my interest in embedded systems, backend engineering, and machine learning into one complete working prototype.
Feel free to connect or reach out: 📧 vm6923@rit.edu | vetrim2003@gmail.com
📌 WHAT THIS PROJECT DOES:
This project implements a complete IoT-based smart security gate system using RFID authentication, real-time monitoring dashboard, and hybrid anomaly detection combining rule-based logic and Isolation Forest machine learning.
🔧 HARDWARE USED:
- Arduino UNO R3
- RC522 RFID Reader (13.56 MHz)
- Grove Servo Motor
- Grove LEDs (Red, Green, Blue)
- Grove Buzzer
- Grove LCD RGB Backlight v4.0
- Grove Base Shield
💻 SOFTWARE STACK:
- Arduino C/C++ — Edge firmware
- Python 3 — Backend
- FastAPI — REST API
- SQLite + SQLAlchemy ORM — Database
- PySerial — Serial communication
- Scikit-learn — Isolation Forest ML
- Chart.js — Dashboard visualization
⚙️ KEY FEATURES:
✅ RFID card authentication at the edge
✅ IN/OUT toggle tracking per user
✅ Real-time web dashboard (auto-refresh 5s)
✅ Today's stats — resets at midnight
✅ User presence tracking (Inside/Outside)
✅ Date and time range log filtering
✅ Per-user behavioral analytics page
✅ Anomaly detection — 3 rule-based checks
✅ Isolation Forest ML — unusual pattern detection
✅ Fully local — no cloud dependency
🚨 ANOMALY DETECTION:
Rule 1 → Off-hours access detection
Rule 2 → Rapid scan detection (sliding window)
Rule 3 → Consecutive denied streak detection
ML → Isolation Forest (unsupervised learning)
Feature: hour of day per user
Activates after 10+ scan history
📁 GITHUB REPOSITORY:
https://github.com/Vetrivel07/Automated-Logistics-and-Access-Security-Gate-with-Monitoring-and-Behavioral-Analysis
📚 COURSE:
ISTE730 — Foundations of IoT | Spring 2026
Rochester Institute of Technology (RIT)
Видео IoT Security Gate with RFID, Real-Time Dashboard & ML Anomaly Detection | ISTE730 IoT Project канала Vetrivel Maheswaran
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22 апреля 2026 г. 19:34:28
00:07:33
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