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dc.title Neural swarm virus en
dc.contributor.author Truong, Thanh Cong
dc.contributor.author Zelinka, Ivan
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Communications in Computer and Information Science
dc.identifier.issn 1865-0929 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9783030378370
dc.date.issued 2020
utb.relation.volume 1092 CCIS
dc.citation.spage 122
dc.citation.epage 134
dc.event.title 7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019
dc.event.location Maribor
utb.event.state-en Slovenia
utb.event.state-cs Slovinsko
dc.event.sdate 2019-07-10
dc.event.edate 2019-07-12
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer
dc.identifier.doi 10.1007/978-3-030-37838-7_12
dc.relation.uri https://link.springer.com/chapter/10.1007%2F978-3-030-37838-7_12
dc.subject Computer virus en
dc.subject Malware en
dc.subject Neural network en
dc.subject Security en
dc.subject Swarm intelligence en
dc.subject Swarm virus en
dc.description.abstract The dramatic improvements in computational intelligence techniques over recent years have influenced many domains. Hence, it is reasonable to expect that virus writers will taking advantage of these techniques to defeat existing security solution. In this article, we outline a possible dynamic swarm smart malware, its structure, and functionality as a background for the forthcoming anti-malware solution. We propose how to record and visualize the behavior of the virus when it propagates through the file system. Neural swarm virus prototype, designed here, simulates the swarm system behavior and integrates the neural network to operate more efficiently. The virus’s behavioral information is stored and displayed as a complex network to reflect the communication and behavior of the swarm. In this complex network, every vertex is then individual virus instances. Additionally, the virus instances can use certain properties associated with the network structure to discovering target and executing a payload on the right object. © Springer Nature Switzerland AG 2020. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009560
utb.identifier.obdid 43881278
utb.identifier.scopus 2-s2.0-85078476625
utb.source d-scopus
dc.date.accessioned 2020-02-11T10:07:40Z
dc.date.available 2020-02-11T10:07:40Z
utb.ou CEBIA-Tech
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.sponsorship The following grants are acknowledged for the financial support provided for this research: Grant of SGS No. SP2019/137, VSB Technical University of Ostrava. This work was also supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089.
utb.scopus.affiliation Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba, 708 00, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, 760 01, Czech Republic
utb.fulltext.projects SP2019/137
utb.fulltext.projects LO1303
utb.fulltext.projects MSMT-7778/2014
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
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