Machine learning based Malicious URLDetection

The exponential growth of the internet has led to an increase in cyber threats, particularly through the spread of malicious URLs.These URLs, embedded in emails, websites, or social media, are designed to deceive users and lead them to malicious websites that steal data, install malware, or perform phishing attacks.Traditional security mechanisms, such as blacklists and rule-based filters, are often inadequate due to their inability to keep up with the evolving tactics of cybercriminals.In this context, machine learning (ML) emerges as a powerful and adaptive solution for detecting malicious URLs in real time.This study presents a machine learningbased approach for detecting malicious URLs using a supervised classification model trained on a dataset comprising lexical, host-based, and content-based features of URLs.Various algorithms, including Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines, were explored to determine the most effective model.The preprocessing phase involved feature extraction from URLs, including statistical attributes, domain characteristics, and token patterns, followed by normalization and encoding.The dataset was split into training and testing sets to evaluate the performance of different models.Results indicate that tree-based ensemble methods such as Random Forest and Gradient Boosting provided the highest detection accuracy with minimal false positives.The system not only outperforms traditional methods but also adapts to previously unseen patterns of malicious behavior.The proposed model demonstrates strong generalization capabilities and robustness against common evasion techniques.This research emphasizes the significance of integrating intelligent learning-based models into cybersecurity systems for proactive threat detection.