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oai:riubu.ubu.es:10259/72402023-02-16T13:35:40Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Magán-Carrion, Roberto Urda Muñoz, Daniel Díaz-Cano, Ignacio Dorronsoro, Bernabé 2023-01-13T13:24:25Z 2023-01-13T13:24:25Z 2020-03 http://hdl.handle.net/10259/7240 10.3390/app10051775 2076-3417 Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR’16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further. The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovación y Universidades and ERDF for the support provided under contracts RTI2018-100754-B-I00 (iSUN) and RTI2018-098160-B-I00 (DEEPAPFORE). application/pdf eng MDPI Applied sciences. 2020, V. 10, n. 5, e1775 https://doi.org/10.3390/app10051775 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-100754-B-I00/ES/SISTEMAS INTELIGENTES DE TRANSPORTE URBANO SOSTENIBLE/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098160-B-I00/ES/DEEP LEARNING IN AIR POLLUTION FORECASTING Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Network intrusion detection Machine learning Attack detection Communications networks Methodology NIDS Informática Computer science Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Applied Sciences 10 5