Enhancement of Network Security through Machine Learning and Deep Learning Techniques: A Real-Time Intrusion Detection System

Developing a resilient intrusion detection system (IDS) is essential to strengthen network security against advanced cyber threats. This project leverages machine learning (ML) and deep learning (DL) techniques to create an IDS that surpasses traditional signature-based methods by detecting and mitigating attacks in real time. By analyzing abnormal patterns and recognizing attack signatures in network traffic, this system ensures a proactive defense mechanism. Utilizing the KDD 1999 dataset, a comprehensive benchmark for network intrusion detection, the IDS incorporates models such as Support Vector Machines (SVM) and XGBoost (XGB) for high-precision classification and regression tasks. Data preprocessing techniques like Principal Component Analysis (PCA) and RobustScaler enhance scaling, while classifiers such as Decision Trees (DT), Random Forest(RF) and K-Nearest Neighbors (KNN) improve detection accuracy. Model performance is evaluated on metrics such as accuracy, precision and recall scores to ensure reliable detection of diverse intrusion types. By integrating ML and DL, this project demonstrates the power of scalable, real-time detection methods capable of identifying novel attack patterns, marking a significant advancement over static, traditional detection systems.