# Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection

arXiv: https://arxiv.org/abs/2008.02327 · HTML: https://mlsanity.com/paper/2008.02327/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2201.09172/) (2022)
- [Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection](https://mlsanity.com/paper/2207.09902/) (2022)
- [IIsy: Practical In-Network Classification](https://mlsanity.com/paper/2205.08243/) (2022)
- [Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing](https://mlsanity.com/paper/2303.11954/) (2023)
- [Machine learning for sports betting: should model selection be based on accuracy or calibration?](https://mlsanity.com/paper/2303.06021/) (2023)
- [Optimized Ensemble Model Towards Secured Industrial IoT Devices](https://mlsanity.com/paper/2401.05509/) (2024)

## Abstract (arXiv)

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.