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Network Intrusion Detection with XGBoost
| Content Provider | Scilit |
|---|---|
| Author | Gouveia, Arnaldo Correia, Miguel |
| Copyright Year | 2020 |
| Description | XGBoost is a recent machine learning method that has been getting increasing attention. It won Kaggle's Higgs Machine Learning Challenge, among several other Kaggle competitions, because of its performance. In this chapter, we explore the use of XGBoost in the context of anomaly-based network intrusion detection, an area in which there is a considerable gap. We study not only the performance of XGBoost with two recent datasets, but also how to optimize its performance and model parameter choice. We also provide insights into which dataset features are the best for performance tuning. Book Name: Recent Advances in Security, Privacy, and Trust for Internet of Things (IoT) and Cyber-Physical Systems (CPS) |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2018-0-96383-6&isbn=9780429270567&doi=10.1201/9780429270567-6&format=pdf |
| Ending Page | 166 |
| Page Count | 30 |
| Starting Page | 137 |
| DOI | 10.1201/9780429270567-6 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-12-16 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Recent Advances in Security, Privacy, and Trust for Internet of Things (iot) and Cyber-physical Systems (cps) Optimize Xgboost Machine Intrusion Detection Kaggle Chapter Higgs Anomaly |
| Content Type | Text |
| Resource Type | Chapter |
