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Spam detection using linear genetic programming

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dc.title Spam detection using linear genetic programming en
dc.contributor.author Meli, Clyde
dc.contributor.author Nezval, Vítězslav
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Buttigieg, Victor
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9783319978871
dc.date.issued 2019
utb.relation.volume 837
dc.citation.spage 80
dc.citation.epage 92
dc.event.title 23rd International Conference on Soft Computing, MENDEL 2017
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2017-06-20
dc.event.edate 2017-06-22
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-97888-8_7
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-97888-8_7
dc.subject Identification en
dc.subject Linear genetic programming en
dc.subject NP-complete en
dc.subject Security en
dc.subject Spam detection en
dc.description.abstract Spam refers to unsolicited bulk email. Many algorithms have been applied to the spam detection problem and many programs have been developed. The problem is an adversarial one and an ongoing fight against spammers. We prove that reliable Spam detection is an NP-complete problem, by mapping email spams to metamorphic viruses and applying Spinellis’s [30] proof of NP-completeness of metamorphic viruses. Using a number of features extracted from the SpamAssassin Data set, a linear genetic programming (LGP) system called Gagenes LGP (or GLGP) has been implemented. The system has been shown to give 99.83% accuracy, higher than Awad et al.’s [3] result with the Naïve Bayes algorithm. GLGP’s recall and precision are higher than Awad et al.’s, and GLGP’s Accuracy is also higher than the reported results by Lai and Tsai [19]. © Springer Nature Switzerland AG 2019. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008169
utb.identifier.obdid 43880128
utb.identifier.scopus 2-s2.0-85051789860
utb.source d-scopus
dc.date.accessioned 2018-08-30T13:31:25Z
dc.date.available 2018-08-30T13:31:25Z
utb.ou CEBIA-Tech
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.fulltext.affiliation Clyde Meli 1(✉) http://orcid.org/0000-0003-3551-862X , Vitezslav Nezval 1 , Zuzana Kominkova Oplatkova 2 , and Victor Buttigieg 3 1 Department of Computer Information Systems, University of Malta, Msida, Malta clyde.meli@um.edu.mt, vnez@cis.um.edu.mt 2 Department of Informatics and Artificial Intelligence, Tomas Bata University, Zlín, Czech Republic kominkovaoplatkova@fai.utb.cz 3 Department of Communications and Computer Engineering, University of Malta, Msida, Malta victor.buttigieg@um.edu.mt
utb.fulltext.dates -
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utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089 and further it was supported by Grant Agency of the Czech Republic—GACR P103/15/06700S. This research has in part been carried out using computational facilities procured through the European Regional Development Fund, Project ERDF-076 ‘Refurbishing the Signal Processing Laboratory within the Department of CCE’, University of Malta.
utb.scopus.affiliation Department of Computer Information Systems, University of Malta, Msida, Malta; Department of Informatics and Artificial Intelligence, Tomas Bata University, Zlín, Czech Republic; Department of Communications and Computer Engineering, University of Malta, Msida, Malta
utb.fulltext.projects LO1303 (MSMT-7778/2014)
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects GACR P103/15/06700S
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