WebSentinel: A Passive In-Browser Framework for Real-Time Web Vulnerability Detection

In the face of increasingly complex cyber threats, the necessity for robust Network Intrusion Detection Systems (NIDS) has never been greater. Conventional rule based systems often struggle to keep pace with evolving attack methodologies , necessitating the integration of machine learning techniques to bolster detection capabilities. Network attacks like probing, Denial of Service, R2L, and U2R affect countless systems daily. Implementing an NIDS that uses machine learning algorithms enhances the ability to detect and respond to these threats effectively. Machine Learning algorithms to prevent this attack. In the Preprocessing techniques include data cleaning and normalization which help in managing imbalanced datasets and improving the accuracy of detection algorithms. The feature selection is based on CFS-BA is used to determine a subset of the original features to eliminate irrelevant features and dimensionality reduction, which selects the optimal subset based on the correlation between features. In order to increase the detection ability of IDS and prevent the service providers from attack, propose an efficient ML based IDS using Light gradient boosting method and Random Forest algorithms. Which is used to classify anomalies, and identify patterns in complex network traffic data.