Assessing the Impact of Batch-Based Data Aggregation Techniques for Feature Engineering on Machine Learning-Based Network IDSs
- 22 September 2021
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 10 references indexed in Scilit:
- How to design the fair experimental classifier evaluationApplied Soft Computing, 2021
- A Systematic Review of Artificial Intelligence and Machine Learning Techniques for Cyber SecurityPublished by Springer Science and Business Media LLC ,2020
- Artificial Intelligence for Cybersecurity: A Systematic Mapping of LiteratureIEEE Access, 2020
- Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning ApproachesApplied Sciences, 2020
- Multivariate Big Data Analysis for intrusion detection: 5 steps from the haystack to the needleComputers & Security, 2019
- UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSsComputers & Security, 2018
- PCA-based multivariate statistical network monitoring for anomaly detectionComputers & Security, 2016
- Network Anomaly Detection: Methods, Systems and ToolsIEEE Communications Surveys & Tutorials, 2013
- A detailed analysis of the KDD CUP 99 data setPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Random ForestsMachine Learning, 2001