000170328 001__ 170328
000170328 005__ 20260410165451.0
000170328 0247_ $$2doi$$a10.1016/j.mlwa.2026.100888
000170328 0248_ $$2sideral$$a148820
000170328 037__ $$aART-2026-148820
000170328 041__ $$aeng
000170328 100__ $$aPelayo-Benedet, Tomás$$uUniversidad de Zaragoza
000170328 245__ $$aA systematic literature review of adversarial domain generation and defense
000170328 260__ $$c2026
000170328 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170328 5203_ $$aDomain Generation Algorithms (DGAs) have long allowed malware to maintain persistent command and control channels by evading static blocklists. However, this dynamic has evolved into a sophisticated arms race: DGAs are no longer simply random but are now optimized to actively deceive detection systems. This paper presents a systematic literature review analyzing 32 primary studies (2016–2025) at the intersection of algorithmically generated domain detection and adversarial machine learning. We construct a comprehensive taxonomy of the evasion landscape, mapping the progression from simple character perturbations to advanced generative adversarial networks and semantic mimicry. Our analysis reveals two systemic flaws in the state of the art. First, we identify a significant deployment gap, where proposed defenses ignore operational realities, such as strict latency limits and the need for false positive rates below 0.1%. Second, we highlight a serious reproducibility crisis driven by a lack of public code and standardized datasets. We conclude by proposing a roadmap to standar
000170328 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T21-23R$$9info:eu-repo/grantAgreement/ES/MCIU/PID2023-151467OA-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131115A-I00
000170328 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000170328 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170328 700__ $$0(orcid)0000-0001-7982-0359$$aRodríguez, Ricardo J.$$uUniversidad de Zaragoza
000170328 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000170328 773__ $$g24, [17 pp.] (2026), 100888$$tMachine Learning with Applications
000170328 8564_ $$s2692096$$uhttps://zaguan.unizar.es/record/170328/files/texto_completo.pdf$$yVersión publicada
000170328 8564_ $$s2718065$$uhttps://zaguan.unizar.es/record/170328/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170328 909CO $$ooai:zaguan.unizar.es:170328$$particulos$$pdriver
000170328 951__ $$a2026-04-10-13:46:39
000170328 980__ $$aARTICLE