🛍️ OmniMart – Full-stack E-commerce Web App with Log Analysis

💡 A full-stack e-commerce web application built with Laravel that integrates ThumbmarkJS for unique visitor identification and access log generation.
The platform simulates both normal and malicious web activities (SQL Injection, XSS, Brute Force, ...) to generate rich logs used for anomaly detection through Machine Learning models.

🌐 Live Demo

🔗 https://omnimart.ct.ws/

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📘 Table of Contents

• Project Overview
• Objectives
• Features
• Technologies
• How to Run
• Research Context
• Contact

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🧩 Project Overview

OmniMart is a full-stack Laravel-based e-commerce web application designed for two main purposes:

1. Provide a functional and interactive online shopping experience.
2. Generate and analyze web access logs for cybersecurity and machine learning–based anomaly detection.

The platform is deployed online at
🔗 https://omnimart.ct.ws/

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🎯 Objectives

• Deploy a modern and responsive e-commerce web system with Laravel to record a dataset of logs containing a unique ID for each visitor/client.
• Integrate ThumbmarkJS to assign a unique identifier to each visitor session.
• Simulate attack scenarios (SQLi, XSS, brute force) for cybersecurity analysis.
• Produce detailed access logs to support AI-based anomaly detection research.

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⚙️ Features

🛒 E-commerce Functionality

• Product management (CRUD: add, edit, delete, view)
• Shopping cart and checkout system
• User authentication (registration, login, roles)
• Order tracking and management
• Responsive and clean UI design (HTML, CSS, JS)

🧾 Logging & Security

• Unique visitor tracking with ThumbmarkJS (client fingerprinting)
• Automatic generation of detailed access logs
• Detection and recording of suspicious activities
• Simulation of web attacks for dataset enrichment:
  • SQL Injection (SQLi)
  • Cross-Site Scripting (XSS)
  • Brute Force Login Attempts

🧠 Log Analysis

• Structured log storage for machine learning analysis
• Realistic behavior modeling for anomaly detection
• Enriched access logging for each request (visitor ID, IP, user agent, route, params)

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🛠️ Technologies

Layer	Tools / Frameworks
Backend	Laravel · PHP
Frontend	HTML5 · CSS3 · JavaScript
Database	MySQL
Logging	Access Logs · ThumbmarkJS
Security	Web Attack Simulation (SQLi, XSS, Brute Force)
Deployment	InfinityFree Hosting (omnimart.ct.ws)

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🖥️ How to Run

Prerequisites

• PHP 8+
• Composer
• MySQL
• Node.js and npm

Steps

# 1. Clone the repository
git clone https://github.com/abdessamad-chahbi/myapp-omnimart.git
cd myapp-omnimart

# 2. Install PHP dependencies
composer install

# 3. Install frontend dependencies
npm install

# 4. Configure environment variables
cp .env.example .env
php artisan key:generate

# 5. Configure DB credentials in .env

# 6. Run migrations
php artisan migrate --seed

# 7. Create storage symlink for public uploads
php artisan storage:link

# 8. Start the development server
php artisan serve

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🔬 Research Context

This project was deployed as part of my final-year internship (PFE 2025) for the Master's degree "Data Science and Intelligent Systems at the Faculty of Sciences and Techniques of Fez.

🧠 It served as the dataset generator and experimental platform for my thesis and related research on intelligent log analysis.

📍 The main objective of this end-of-studies project was to design and develop an intelligent system for automatic log analysis, capable of effectively exploiting log data to detect anomalies and suspicious behaviors within complex and heterogeneous IT environments.

Key Research Objectives:

• Generate realistic log datasets by simulating normal and malicious web activities.
• Deploy a e-commerce application to produce detailed access logs enriched with unique visitor IDs using ThumbmarkJS.
• Develop a complete automated pipeline for log analysis — from raw data parsing to behavioral anomaly detection, without relying on labeled data.
• Apply unsupervised learning techniques such as K-Means, Isolation Forest, and One-Class SVM for anomaly detection.
• Simulate cyberattack scenarios (SQL Injection, XSS, Brute Force, etc.) to evaluate and strengthen system robustness.

💡 Results: An intelligent system capable of identifying complex anomalies with high detection accuracy — even in the absence of labeled datasets.

📎 For more details, visit the Experience section of my LinkedIn profile: 👉 linkedin.com/in/abdessamad-chahbi

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📬 Contact

• Author: Abdessamad Chahbi
• GitHub: abdessamad-chahbi
• Live Demo: https://omnimart.ct.ws/
• Project URL: myapp-omnimart

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