نمایش مختصر رکورد

dc.contributor.authorSoleimanian Gharehchopogh, Farhaden_US
dc.contributor.authorMousavi, Seyyed Keyvanen_US
dc.date.accessioned1400-03-20T16:18:10Zfa_IR
dc.date.accessioned2021-06-10T16:18:11Z
dc.date.available1400-03-20T16:18:10Zfa_IR
dc.date.available2021-06-10T16:18:11Z
dc.date.issued2020-08-01en_US
dc.date.issued1399-05-11fa_IR
dc.date.submitted2019-07-06en_US
dc.date.submitted1398-04-15fa_IR
dc.identifier.citationSoleimanian Gharehchopogh, Farhad, Mousavi, Seyyed Keyvan. (2020). A New Feature Selection in Email Spam Detection by Particle Swarm Optimization and Fruit Fly Optimization Algorithms. Computer and Knowledge Engineering, 2(2), 49-62. doi: 10.22067/cke.v2i2.81750en_US
dc.identifier.urihttps://dx.doi.org/10.22067/cke.v2i2.81750
dc.identifier.urihttps://cke.um.ac.ir/article_26945.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/830854
dc.description.abstractWith the advent of the internet, along with email, and social networking, there are some new issues that have caused vulnerability of users against attackers. Internet users face a lot of undesirable emails and their data privacy and security is in danger. Spammers are often sent to users by intruders and sales markets, and most of the time they target spam, harassment, and abuse of user data. With increasing attacks on computer networks, attempts to rebuild computer networks and detect spam emails are important. Hackers use the identities of users by obtaining their personal information and account of users for malicious and subversive actions. Intruders are attempting to expose, remove, or change user information by opening encrypted information. Therefore, it is very important to detect spam in the early stages. In this paper, a new approach is proposed based on a hybridization of Particle Swarm Optimization (PSO) with Fruit Fly Optimization (FFO) to email spam detection. This paper shows a Feature Selection (FS) based on PSO, which decreases dimensionality and improves the accuracy of email spam classification. The PSO searches the feature space for the best feature subsets. Experiments results on the public spambase dataset show that the accuracy of the proposed model is 92.21%, which is better in comparison with others models, such as PSO, Genetic Algorithm (GA), and Ant Colony Optimization (ACO).en_US
dc.format.extent639
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.relation.ispartofComputer and Knowledge Engineeringen_US
dc.relation.isversionofhttps://dx.doi.org/10.22067/cke.v2i2.81750
dc.subjectEmail spam detectionen_US
dc.subjectFeature selectionen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectFruit fly optimizationen_US
dc.titleA New Feature Selection in Email Spam Detection by Particle Swarm Optimization and Fruit Fly Optimization Algorithmsen_US
dc.typeTexten_US
dc.typeMachine Learning - Monsefien_US
dc.contributor.departmentUrmia Branch, Islamic Azad University, Urmia, IRANen_US
dc.contributor.departmentDepartment of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRANen_US
dc.citation.volume2
dc.citation.issue2
dc.citation.spage49
dc.citation.epage62


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