RT info:eu-repo/semantics/article
T1 Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches
A1 Magán-Carrion, Roberto
A1 Urda Muñoz, Daniel
A1 Díaz-Cano, Ignacio
A1 Dorronsoro, Bernabé
K1 Network intrusion detection
K1 NIDS
K1 Machine learning
K1 Attack detection
K1 Communications networks
K1 Methodology
K1 Informática
K1 Computer science
AB Presently, we are living in a hyper-connected world where millions of heterogeneousdevices are continuously sharing information in different application contexts for wellness, improvingcommunications, digital businesses, etc. However, the bigger the number of devices and connectionsare, the higher the risk of security threats in this scenario. To counteract against malicious behavioursand preserve essential security services, Network Intrusion Detection Systems (NIDSs) are themost widely used defence line in communications networks. Nevertheless, there is no standardmethodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioningcrucial 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 learningapproaches, concluding that almost all of them do not accomplish with what authors of this paperconsider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structuredmethodology is proposed and assessed on the UGR’16 dataset to test its suitability for addressingnetwork attack detection problems. The guideline and steps recommended will definitively helpthe research community to fairly assess NIDSs, although the definitive framework is not a trivialtask and, therefore, some extra effort should still be made to improve its understandability andusability further.
PB MDPI
YR 2020
FD 2020-03
LK http://hdl.handle.net/10259/7240
UL http://hdl.handle.net/10259/7240
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 11-dic-2024