Exploring an Artificial Arms Race for Malware Detection
| dc.contributor.author | Wilkins Z. | |
| dc.contributor.author | Zincir I. | |
| dc.contributor.author | Zincir-Heywood N. | |
| dc.date.accessioned | 2023-06-16T15:01:55Z | |
| dc.date.available | 2023-06-16T15:01:55Z | |
| dc.date.issued | 2020 | |
| dc.description | ACM SIGEVO | en_US |
| dc.description | 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 -- 8 July 2020 through 12 July 2020 -- 161684 | en_US |
| dc.description.abstract | The 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. | en_US |
| dc.description.sponsorship | Natural Sciences and Engineering Research Council of Canada, NSERC | en_US |
| dc.description.sponsorship | This research is supported partly by the Natural Science and Engineering Research Council of Canada (NSERC). This research is conducted as part of the Dalhousie NIMS Lab at: https://projects.cs. dal.ca/projectx/. | en_US |
| dc.identifier.doi | 10.1145/3377929.3398090 | |
| dc.identifier.isbn | 9.78E+12 | |
| dc.identifier.scopus | 2-s2.0-85089735660 | |
| dc.identifier.uri | https://doi.org/10.1145/3377929.3398090 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14365/3671 | |
| dc.language.iso | en | en_US |
| dc.publisher | Association for Computing Machinery, Inc | en_US |
| dc.relation.ispartof | GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Android | en_US |
| dc.subject | Cyber attack | en_US |
| dc.subject | Cyber security | en_US |
| dc.subject | Evolution | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Malware | en_US |
| dc.subject | Mobile security | en_US |
| dc.subject | Smartphone | en_US |
| dc.subject | Android (operating system) | en_US |
| dc.subject | Artificial limbs | en_US |
| dc.subject | Malware | en_US |
| dc.subject | Android malware | en_US |
| dc.subject | Android platforms | en_US |
| dc.subject | Artificial arms | en_US |
| dc.subject | Malware detection | en_US |
| dc.subject | Mobile markets | en_US |
| dc.subject | Mobile security | en_US |
| dc.title | Exploring an Artificial Arms Race for Malware Detection | en_US |
| dc.type | Conference Object | en_US |
| dspace.entity.type | Publication | |
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| gdc.description.departmenttemp | Wilkins, Z., Dalhousie University, Computer Science, Halifax, NS, Canada; Zincir, I., Izmir University of Economics, Software Engineering, Izmir, Turkey; Zincir-Heywood, N., Dalhousie University, Computer Science, Halifax, NS, Canada | en_US |
| gdc.description.endpage | 1545 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.startpage | 1537 | en_US |
| gdc.description.wosquality | N/A | |
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| gdc.oaire.sciencefields | 0202 electrical engineering, electronic engineering, information engineering | |
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