This constitutes a model and web-based python system for detection of anomalies in water consumption

A lightweight Intrusion Detection System (IDS) built with Python and Flask to analyze network traffic, detect anomalies, and visualize alerts in real time.

ATM Banking System with AI/ML Fraud Detection A full-stack Python Flask + SQLite banking system with a modern HTML frontend and AI-powered real-time fraud, anomaly, and spending analytics. Perfect for demo, college projects, and ML learning.

QR-based smart attendance system with ML risk scoring, anomaly detection, and detention prediction. Built with Python, Flask, and SQLite.

AI-powered surveillance solution with Python, OpenCV, YOLOv8, Flask, React.js, and MongoDB. Includes real-time crowd detection, weapon recognition, anomaly monitoring, and live dashboard alerts.

This project is an Anomaly Detection System for System Logs that uses machine learning with python to identify unusual patterns in log data. It features a Flask backend, Streamlit dashboard, and supports real-time anomaly alerts.

Python application for automated transaction data monitoring and anomaly detection. Built with Flask backend, pandas for data processing, and HTML report for visualization. The system cyclically analyzes CSV transaction data, generates reports, sends email notifications, and looks for any anomaly in sales values.

A real-time network packet anomaly detection tool using Python and Scapy. It detects suspicious traffic using ML, logs packets, and integrates with VirusTotal and Shodan for threat intelligence. Features include alerts, CSV logging, and optional Flask dashboard for live monitoring.

A machine learning-powered web application for detecting anomalies in network traffic using Isolation Forest and Logistic Regression. Built with Python, Flask, and scikit-learn, the app offers a simple dashboard to upload network data, visualize detection results, and flag suspicious behavior in real-time.

Secure centralized TFTP infrastructure with monitoring, anomaly detection and real-time dashboard (Python, Flask, MySQL, Syslog).

Real time machine monitoring system with anomaly detection and interactive dashboard using Python, Flask, and sensor data analytics.

AI-powered UPI fraud detection system using supervised learning, anomaly detection, graph analysis, and SHAP explainability. Built with Python, Flask, XGBoost, and LightGBM.

Secure web application built with Flask and Python Cryptography for encrypting and decrypting text and files with AI-based anomaly detection module.

AI-powered self-healing cloud infrastructure system with anomaly detection, automated recovery, and real-time monitoring dashboard built using Python, ML, and Flask.

Visual Network Tracker — A Python Flask-based network anomaly detection system with MITRE ATT&CK simulation, real-time monitoring, and multi-mode dashboard (Simple, Executive, Analyst).

Real-time Stock Market Anomaly Detection Dashboard using Python, Flask, and Machine Learning (Isolation Forest). Detects price outliers in Yahoo Finance data with interactive Plotly visualizations.

A Flask-based web application for Credit Card Fraud Detection using anomaly detection algorithms from PyOD. Includes real-time fraud detection, interactive visualizations, and a simple web interface to input transaction details. Built with Python, Flask, HTML/CSS, and JavaScript for easy deployment and use.

A real-time system performance monitor leveraging Machine Learning (Isolation Forest & Linear Regression) for predictive resource analysis, anomaly detection, and automated process optimization. Built with Python, Flask, and React.

Fraud Detection in Credit Card Transactions using Python, Scikit-learn, and XGBoost. Includes anomaly detection (Isolation Forest, LOF), supervised learning, data preprocessing, and evaluation with ROC/confusion matrix. A Streamlit/Flask dashboard enables predictions and deployment.

Technologies: Python, Scikit-learn, XGBoost, Flask, Redis Implemented ML-based anomaly detection for financial transactions Achieved 92% precision with sub-100ms real-time predictions Successfully prevented fraudulent transactions in production

A real-time network anomaly detection system using machine learning (Isolation Forest) to detect DDoS attacks. Features automatic IP throttling/blocking, traffic monitoring, Flask-based web server, and Docker support. Built with Python, scikit-learn, and Flask. Educational/research purposes only.

Real-time Hybrid IDPS combining Signature-Based detection and AI Anomaly Detection (Isolation Forest). Detects known malware (Worms, Viruses) and zero-day threats. Built with Python, Scapy, Flask, and Tkinter, featuring a live dashboard, SQLite logging, and active IP auto-blocking.

secure flask digital wallet with p2p payments, cash management & real-time fraud detection. features bcrypt auth, json storage, transaction history, admin dashboard & audit logs. detects suspicious activity, large transfers & frequency anomalies. python backend with financial-grade security.

MiniSIEM: Lightweight open-source SIEM tool for rapid threat detection, log analysis & alerting. Real-time aggregation from syslog/files/APIs, rule-based anomaly detection, searchable dashboard with Oracle DB integration. Python/Flask backend, Docker deployment. Low-resource (<100MB), perfect for homelabs/small teams.

Campus Abnormal Behavior Recognition detects suspicious activities using Temporal Segment Transformers (TST), YOLOv8, and CNNs. Built with Python (Flask), HTML, CSS, and JavaScript, it analyzes real-time and recorded footage for anomaly detection. Flask ensures lightweight deployment, making it ideal for campus security monitoring.

Real-Time Fraud Analytics detects fraudulent transactions instantly using Python, Flask, and ML. It processes high-volume streams, applies anomaly detection, classifies transactions (approve/flag/reject), and provides a real-time dashboard with alerts, metrics, and KPIs.

🚦 Full-stack data pipeline that connects environmental (air quality, weather) and economic data sources, performs anomaly detection and forecasting, and delivers interactive visual insights via a Flask dashboard. Built with Python, MongoDB, PostgreSQL, and machine learning models.

A real-time AI anomaly detection pipeline using Apache Kafka for high-throughput data streaming and a Flask-based web dashboard for live monitoring. Built with Python and Scikit-learn to detect and visualize outliers in streaming datasets.

Hybrid Intrusion Detection System is a cybersecurity project that detects network attacks using CNN and Autoencoder models. Built with Python and Flask, it analyzes network traffic to identify anomalies and intrusions in real time, improving detection accuracy and strengthening network security using the NSL-KDD dataset.

Attendance Analysis System AI-powered attendance tracking using Google's Gemini API to process handwritten sheets. Features OCR, defaulter identification, anomaly detection, and analytics dashboard. Built with Python Flask, Pandas, and Pillow. Streamlines attendance management for educators and institutions.