Intrusion Detection Using Machine Learning Classification and Regression

An Intrusion Detection System (IDS) is a crucial security mechanism designed to protect computer networks from unauthorized access and cyber threats. With the rapid expansion of Internet-based data transmission, ensuring network security has become increasingly challenging. IDS continuously monitors and analyzes network traffic to detect malicious activities, relying on datasets like KDD Cup 1999 for training and evaluation. Effective IDS development involves preprocessing steps such as feature selection, normalization, and addressing data imbalance to enhance detection accuracy. Various machine learning techniques, including Decision Trees, Support Vector Machines, Neural Networks, Bayesian Networks, and ensemble methods, are employed to classify network traffic as normal or malicious. IDS performance is assessed using accuracy, precision, recall, and F1-score, with cross-validation and hyperparameter tuning improving model robustness. Key challenges include handling dynamic network traffic, achieving real-time scalability, and minimizing false positives and false negatives. As cyber threats continue to evolve, advancements in artificial intelligence and deep learning are driving the development of adaptive IDS capable of detecting and responding to emerging attacks in real time. Keywords: Network Security, KDD Cup 1999 dataset, Machine Learning, Data Mining, Anomaly Detection, Cybersecurity, Preprocessing, Classification Algorithms, Accuracy Metrics, Model Validation, Ensemble Methods, False Positives, False Negatives, Scalability, Dynamic Network Traffic, Hyperparameter Tuning, Real-time Monitoring, Evolving Intrusion Tactics