• Cross-device behavioral consistency : benchmarking and implications for effective android malware detectionGuerra Manzanares, Alejandro; Välbe, MartinMachine Learning with Applications2022 / art. 100357, 15 p. : ill https://doi.org/10.1016/j.mlwa.2022.100357
• Machine learning for android malware detection : mission accomplished? a comprehensive review of open challenges and future perspectivesGuerra-Manzanares, AlejandroComputers and Security2024 / art. 103654 https://doi.org/10.1016/j.cose.2023.103654 https://www.scopus.com/sourceid/28898 https://www.scopus.com/record/display.uri?eid=2-s2.0-85181774264&origin=resultslist&sort=plf-f&src=s&sot=b&sdt=b&s=DOI%2810.1016%2Fj.cose.2023.103654%29&sessionSearchId=971b6d536926a71d00c3665b57fa29cc&relpos=0 https://jcr.clarivate.com/jcr-jp/journal-profile?journal=COMPUT%20SECUR&year=2023 https://www.webofscience.com/wos/woscc/full-record/WOS:001155918900001
• Mobiilse pahavara plahvatusele võib pakkuda leevendust masinõpe [Võrguväljaanne]Oidermaa, Jaan-Juhannovaator.err.ee2022 https://novaator.err.ee/1608708055/mobiilse-pahavara-plahvatusele-voib-pakkuda-leevendust-masinope
• On the application of active learning to handle data evolution in Android malware detectionGuerra Manzanares, Alejandro; Bahsi, HayretdinDigital forensics and cyber crime : 13th EAI international conference, ICDF2C 2022, Boston, MA, November 16-18, 2022 : proceedings2023 / p. 256-273 https://doi.org/10.1007/978-3-031-36574-4_15