Cybersecurity
Machine Learning and AutoML for Network Intrusion Detection: A Cybersecurity Research Case Study
- machine learning
- artificial intelligence
- AutoML
- automated machine learning
- network intrusion detection
- cybersecurity
- ensemble learning
- stacked ensemble
- NSL-KDD dataset
MLJAR tools were used in the following publication.
An AutoML-based approach for Network Intrusion Detection
Gyimah, Nana Kankam, Mwakalonge, Judith, Comert, Gurcan, Siuhi, Saidi, Akinie, Robert, Sulle, Methusela, Ruganuza, Denis, Izison, Benibo, Mukwaya, Arthur
South Carolina State University, Orangeburg, South Carolina, USA | North Carolina A&T State University, Greensboro, North Carolina, USA
This research presents a machine learning and AutoML framework for network intrusion detection in cybersecurity. Using the MLJAR AutoML platform, the authors developed a stacked ensemble model combining LightGBM, XGBoost, and CatBoost to improve detection accuracy and reduce false positives. Evaluated on the NSL-KDD dataset, the AutoML-driven approach achieved significantly higher performance than individual machine learning models. The study demonstrates how automated machine learning can enhance predictive analytics and strengthen modern network security systems.
SoutheastCon 2025 • November 24, 2024
Research Domains
Explore peer-reviewed and applied machine learning studies across diverse domains, including healthcare analytics, financial modeling, manufacturing optimization, and structured data classification problems.
Why Researchers and ML Engineers Choose MLJAR Studio
A private, AI-powered Python notebook designed for reproducible machine learning experiments, structured benchmarking, and applied research workflows - fully under your control.
Reproducible Machine Learning Experiments
Design structured pipelines, save experiment runs, and compare results across iterations with full transparency. Every validation setup, hyperparameter configuration, and model benchmark is recorded - making your research repeatable and defensible.
Local-First Execution & Data Control
Run all workflows directly on your machine. Sensitive datasets remain private, with no mandatory cloud uploads or external AI services required. Maintain full control over runtime environments and compliance requirements.
Autonomous Model Benchmarking & Optimization
Automatically compare candidate models, perform cross-validation, and run hyperparameter optimization while retaining full visibility into generated Python code and evaluation metrics. Accelerate experimentation without sacrificing methodological rigor.
Build Research-Grade ML Workflows Locally
Run automated model benchmarking, hyperparameter optimization, and autonomous experiments while keeping full control over your data.