Machine Learning-Driven Detection of Malicious URLs and Network Intrusions

The detection of malicious URLs and network intrusions is a critical task in cybersecurity. This study investigates the application of several machine learning models to classify network traffic data for intrusion detection purposes. The dataset, obtained, comprises 4,998 instances with 32 features related to network traffic attributes. Data preprocessing steps including normalization and feature selection based on information gain were applied. The study evaluates the performance of various classification algorithms, such as Support Vector Machines (SVM), AdaBoost, Random Forest, XGBoost, and Gaussian Naive Bayes. The XGBoost classifier achieves the highest accuracy of 92.1 %, demonstrating its effectiveness in detecting intrusions across several intrusion classes. The results highlight the potential of machine learning techniques in enhancing cybersecurity through malicious URLs and intrusion detection.