# Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection

arXiv: https://arxiv.org/abs/2207.09902 · HTML: https://mlsanity.com/paper/2207.09902/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets](https://mlsanity.com/paper/1605.07079/) (2016)
- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection](https://mlsanity.com/paper/2008.02327/) (2020)
- [Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach](https://mlsanity.com/paper/2012.11325/) (2020)
- [Malware Detection and Prevention using Artificial Intelligence Techniques](https://mlsanity.com/paper/2206.12770/) (2022)
- [Weighted Random Search for CNN Hyperparameter Optimization](https://mlsanity.com/paper/2003.13300/) (2020)

## Built on by (most cited first, 5 of 5)

- [Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data](https://mlsanity.com/paper/2401.14544/) (2024)
- [Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms](https://mlsanity.com/paper/2306.08060/) (2023)
- [EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code](https://mlsanity.com/paper/2411.16561/) (2024)
- [Interval Prediction of Annual Average Daily Traffic on Local Roads via Quantile Random Forest with High-Dimensional Spatial Data](https://mlsanity.com/paper/2510.18548/) (2025)
- [DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning](https://mlsanity.com/paper/2603.15540/) (2026)

## Abstract (arXiv)

Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework's effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score.