Intrusion Detection Using Machine Learning and Deep Learning

As cyberattacks grow in prevalence, Intrusion Detection Systems (IDS) have become critical for securing network infrastructures. This study proposes an efficient IDS framework utilizing both machine learning (ML) and deep learning (DL) algorithms. The framework is evaluated on the “NF-UNSW-NB15-v2” dataset, which comprises a blend of normal and malicious traffic. A diverse set of advanced models—including Deep Neural Networks (DNN), Long Short-Term Memory (LSTM) networks, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN)—is deployed for intrusion detection. The approach encompasses both binary classification (normal vs. malicious) and multi-class classification (specific attack categories). Preprocessing steps include feature standardization using StandardScaler, class imbalance correction via SMOTE, and dimensionality reduction through Principal Component Analysis (PCA). Results show that Random Forest and XGBoost models achieve high accuracy in binary classification with F1-scores approaching 0.97, while XGBoost attains the best macro F1-score (0.71) in multi-class tasks. Additionally, RF and XGBoost demonstrate the fastest inference times, underscoring their suitability for real-time deployment. This work contributes a scalable and optimized IDS pipeline for enhancing cybersecurity resilience.

@article{ALzaher2025,
  title     = {Intrusion Detection Using Machine Learning and Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Fatima Jobran ALzaher and Asma AlJarullah},
  doi       = {10.14569/IJACSA.2025.0160844},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160844}
}