Machine Learning Approaches for Intrusion Detection in Computer Networks

Abstract

Intrusion detection is a central component of network defense because firewalls and signature rules alone cannot reliably identify previously unseen or rapidly evolving attacks. This methodology paper presents a systematic framework for applying machine-learning (ML) and deep-learning (DL) approaches to network intrusion detection using benchmark traffic data available in the literature up to 2019. The proposed procedure integrates dataset selection, cleaning, categorical encoding, feature scaling, imbalance treatment, feature selection, classifier training, hyperparameter tuning, cross-validation, and final testing. Classical models such as k-nearest neighbors, support vector machines and random forests are compared with multilayer neural networks, recurrent neural networks and autoencoder-based anomaly detectors. Performance is assessed with accuracy, precision, recall, F1-score, false-positive rate, receiver-operating characteristics and computational cost rather than accuracy alone. The methodology emphasizes leakage-free data partitioning and evaluation on modern datasets such as UNSW-NB15 and CICIDS2017 in addition to NSL-KDD. Literature through 2019 shows that deep models can learn complex traffic representations, while classical models remain valuable for efficiency and interpretability. The paper therefore recommends a comparative, dataset-aware evaluation strategy in which detection quality, false alarms, generalization and deployment cost are considered jointly.