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Web History Analysis - Wiki

Problem

In today's digital age, analyzing web browsing history is crucial for various purposes such as cybersecurity, data analysis, parental control, and user behavior understanding. However, manually analyzing large sets of URLs from browser history logs is a tedious and inefficient task. Traditional methods often fail to classify URLs into meaningful categories, leaving users with unstructured and confusing data. Security researchers, data analysts, and organizations need a robust way to automatically categorize URLs based on browsing patterns.

Key Challenges

Large volumes of browsing history data make manual classification impossible.

URLs often contain complex structures, numbers, and special characters.

Lack of efficient tools to automatically classify and label URLs based on their content or domain.

Security researchers need quick insights into browsing patterns to detect malicious or suspicious activity.

Solution

Web History Analysis is an advanced machine learning-based tool developed by YogSec. It aims to solve the problem of URL classification using deep learning techniques, specifically an LSTM (Long Short-Term Memory) network. This tool is designed to automate the classification of URLs into predefined categories, making it easier to analyze large datasets from browser history logs.

Key Features

URL Classification: Automatically categorizes URLs from browsing history logs.

Machine Learning Integration: Utilizes an LSTM neural network trained on labeled data for accurate predictions.

Preprocessing Capabilities: Cleans URLs by removing protocols, numbers, and special characters.

File Input Support: Accepts URL data from CSV or text files for batch processing.

Model Evaluation: Provides accuracy reports after model training and evaluation.