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Shallow-and-deep-learning-based-approaches-for-malicious-URL-detection

The main purpose is to identify and categorize dangerous URLs that are used these days to make people fall prey to hackers, and to assess these we use deep and machine learning models that classify URLs effectively.

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System Overview

What the project does

Detects and classifies potentially malicious URLs using both shallow (traditional machine‑learning) and deep‑learning models.

Key features

  • - Implements multiple ML and DL classifiers for URL analysis.
  • - Enables comparative evaluation of shallow vs. deep approaches.
  • - Provides a framework for training, testing, and scoring URL datasets.

    • Tech stack

    Python + scikit‑learn (shallow models) and TensorFlow/Keras or PyTorch (deep models), with typical data‑processing libraries (pandas, NumPy).

    Use case

    Cybersecurity tool for phishing‑prevention and threat intelligence, helping organizations automatically flag risky web links.

    Architecture Details

    This system integrates multiple components for a seamless automation flow. Structural interpretation based on project focus:

    Backend Infrastructure

    Core execution layer for robust data processing and API handling.

    AI / Logic Core

    Intelligent decisioning via models or logical workflow rules.

    Tech Stack

    Jupyter NotebookIntegrationAutomationAPIs

    Key Capabilities

    • ▹ Custom workflow execution
    • ▹ Data transformation and routing
    • ▹ Extensible architecture