Wilkins Z.Zincir I.Zincir-Heywood N.2023-06-162023-06-1620209.78E+12https://doi.org/10.1145/3377929.3398090https://hdl.handle.net/20.500.14365/3671ACM SIGEVO2020 Genetic and Evolutionary Computation Conference, GECCO 2020 -- 8 July 2020 through 12 July 2020 -- 161684The Android platform commands a dramatic majority of the mobile market, and this popularity makes it an appealing target for malicious actors. Android malware is especially dangerous because of the versatility in distribution and acquisition of software on the platform. In this paper, we continue to investigate evolutionary Android malware detection systems, implementing new features in an artificial arms race, and comparing different systems' performances on three new datasets. Our evaluations show that the artificial arms race based system achieves the overall best performance on these very challenging datasets. © 2020 ACM.eninfo:eu-repo/semantics/openAccessAndroidCyber attackCyber securityEvolutionMachine learningMalwareMobile securitySmartphoneAndroid (operating system)Artificial limbsMalwareAndroid malwareAndroid platformsArtificial armsMalware detectionMobile marketsMobile securityConference Object10.1145/3377929.33980902-s2.0-85089735660