ORCID Profile
0000-0001-7297-9199
Current Organisations
Universidad de La Frontera
,
University of St Andrews
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Publisher: MDPI AG
Date: 30-04-2019
DOI: 10.3390/INFO10050159
Abstract: Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the accuracy of anomaly-based network intrusion detection systems (IDS) that are built using predictive models in a batch learning setup. This work investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these intrusion detection models. Specifically, this research studied the adaptability features of three well known machine learning algorithms: C5.0, Random Forest and Support Vector Machine. Each algorithm’s ability to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective s ling approach. Multiple IDS datasets were used for the analysis, including a newly generated dataset (STA2018). This research demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation traffic have different statistical properties. Tests were undertaken to analyse the effects of feature selection and data balancing on model accuracy when different significant features in traffic were used. The effects of threshold adaptation on improving accuracy were statistically analysed. Of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates.
Publisher: IEEE
Date: 2010
DOI: 10.1109/SUTC.2010.46
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 14-06-2021
Publisher: Elsevier BV
Date: 11-2023
Publisher: Wiley
Date: 12-01-2012
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 07-2013
DOI: 10.1109/NAS.2013.9
Publisher: No publisher found
Date: 2018
Publisher: IEEE
Date: 2005
Publisher: Zenodo
Date: 2018
Publisher: Informa UK Limited
Date: 03-04-2019
Publisher: Informa UK Limited
Date: 02-2011
Publisher: Hindawi Limited
Date: 25-03-2018
DOI: 10.1111/ARE.13663
Publisher: The Haworth Press
Date: 10-11-2004
Publisher: IEEE
Date: 06-2020
Publisher: IEEE
Date: 06-2019
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Ishbel Duncan.