Paper Title
Extra Trees Approach for URLs Intrusion Detection for Network Security

Abstract
The goal of this advanced cybersecurity project is to develop a dynamic, real-time intrusion detection system (IDS) that integrates Apache Spark with machine learning for enhanced scalability and accuracy. This system combines signature-based and anomaly-based detection techniques to detect both known and emerging cyber threats. Signature-based detection identifies attacks by comparing network traffic with known attack signatures, while anomaly-based detection highlights deviations from normal behavior to uncover new threats. Leveraging Apache Spark's in-memory processing capabilities, the IDS efficiently analyzes large-scale network traffic in real time and achieves an impressive detection accuracy of up to 99%. The project employs advanced machine learning techniques, including the ExtraTrees model for feature selection and classification, as well as ensemble methods and neural networks to enhance detection performance. By utilizing these techniques, the system effectively models complex attack patterns, ensuring proactive, scalable, and intelligent security solutions against evolving cyber threats. Keywords - Intrusion Detection, Network Security, Extra Trees, Apache Spark, CISID 2017 Dataset, Signature-based Detection, Anomaly-based Detection, Neural Networks, Ensemble Methods, Real-time Processing, Cybersecurity, Threat Detection, Scalable Defense, Proactive Security.