Data Security and Protection: A Mechanism for Managing Data Theft and Cybercrime in Online Platforms of Educational Institution

Phishing attacks, which use fake URLs to imitate trustworthy websites, are still a constant and developing cybersecurity risk. The machine learning method for automated phishing URL detection is examined in this research. We introduce a system that uses a Random Forest Classifier that was learned using features that were taken straight out of URL strings. The length of the URL, whether the 'https' protocol indicator is present, and the number of particular special characters are some examples of these characteristics. Our system, which is implemented in Python and uses tools like Flask for API deployment, pandas for data manipulation, and scikit-learn for model creation, shows a workable and effective approach. On a dataset of phishing URLs, experimental testing shows that the trained Random Forest Classifier attains a classification accuracy of 0.89.The effectiveness of using straightforward, computationally cheap URL-based characteristics in combination with ensemble learning approaches for successful preliminary phishing detection is demonstrated by this finding. The developed system provides an easily integrable component for improved web security applications and real-time URL analysis, and it is available through a RESTful API.