000145168 001__ 145168
000145168 005__ 20250923084440.0
000145168 0247_ $$2doi$$a10.1016/j.iot.2024.101367
000145168 0248_ $$2sideral$$a139983
000145168 037__ $$aART-2024-139983
000145168 041__ $$aeng
000145168 100__ $$0(orcid)0000-0001-9485-7678$$aGarcía, José$$uUniversidad de Zaragoza
000145168 245__ $$aEmpirical evaluation of feature selection methods for machine learning based intrusion detection in IoT scenarios
000145168 260__ $$c2024
000145168 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145168 5203_ $$aThis paper delves into the critical need for enhanced security measures within the Internet of Things (IoT) landscape due to inherent vulnerabilities in IoT devices, rendering them susceptible to various forms of cyber-attacks. The study emphasizes the importance of Intrusion Detection Systems (IDS) for continuous threat monitoring. The objective of this study was to conduct a comprehensive evaluation of feature selection (FS) methods using various machine learning (ML) techniques for classifying traffic flows within datasets containing intrusions in IoT environments. An extensive benchmark analysis of ML techniques and FS methods was performed, assessing feature selection under different approaches including Filter Feature Ranking (FFR), Filter-Feature Subset Selection (FSS), and Wrapper-based Feature Selection (WFS). FS becomes pivotal in handling vast IoT data by reducing irrelevant attributes, addressing the curse of dimensionality, enhancing model interpretability, and optimizing resources in devices with limited capacity. Key findings indicate the outperformance for traffic flows classification of certain tree-based algorithms, such as J48 or PART, against other machine learning techniques (naive Bayes, multi-layer perceptron, logistic, adaptive boosting or k-Nearest Neighbors), showcasing a good balance between performance and execution time. FS methods' advantages and drawbacks are discussed, highlighting the main differences in results obtained among different FS approaches. Filter-feature Subset Selection (FSS) approaches such as CFS could be more suitable than Filter Feature Ranking (FFR), which may select correlated attributes, or than Wrapper-based Feature Selection (WFS) methods, which may tailor attribute subsets for specific ML techniques and have lengthy execution times. In any case, reducing attributes via FS has allowed optimization of classification without compromising accuracy. In this study, F1 score classification results above 0.99, along with a reduction of over 60% in the number of attributes, have been achieved in most experiments conducted across four datasets, both in binary and multiclass modes. This work emphasizes the importance of a balanced attribute selection process, taking into account threat detection capabilities and computational complexity.
000145168 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T31-20R$$9info:eu-repo/grantAgreement/ES/MCINN/PID2022-136476OB-I00$$9info:eu-repo/grantAgreement/ES/UZ/UZ2021-TEC-01
000145168 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145168 590__ $$a7.6$$b2024
000145168 592__ $$a1.527$$b2024
000145168 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b16 / 258 = 0.062$$c2024$$dQ1$$eT1
000145168 591__ $$aTELECOMMUNICATIONS$$b13 / 120 = 0.108$$c2024$$dQ1$$eT1
000145168 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b33 / 366 = 0.09$$c2024$$dQ1$$eT1
000145168 593__ $$aArtificial Intelligence$$c2024$$dQ1
000145168 593__ $$aEngineering (miscellaneous)$$c2024$$dQ1
000145168 593__ $$aComputer Science (miscellaneous)$$c2024$$dQ1
000145168 593__ $$aSoftware$$c2024$$dQ1
000145168 593__ $$aHardware and Architecture$$c2024$$dQ1
000145168 593__ $$aInformation Systems$$c2024$$dQ1
000145168 593__ $$aManagement of Technology and Innovation$$c2024$$dQ1
000145168 593__ $$aComputer Science Applications$$c2024$$dQ1
000145168 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145168 700__ $$aEntrena, Jorge
000145168 700__ $$0(orcid)0000-0002-5254-1402$$aAlesanco, Álvaro$$uUniversidad de Zaragoza
000145168 7102_ $$15008$$2560$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Ingeniería Telemática
000145168 773__ $$g28 (2024), 101367 [18 pp.]$$tInternet of Things (Netherlands)$$x2542-6605
000145168 8564_ $$s1687436$$uhttps://zaguan.unizar.es/record/145168/files/texto_completo.pdf$$yVersión publicada
000145168 8564_ $$s1870827$$uhttps://zaguan.unizar.es/record/145168/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145168 909CO $$ooai:zaguan.unizar.es:145168$$particulos$$pdriver
000145168 951__ $$a2025-09-22-14:50:06
000145168 980__ $$aARTICLE