Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/9390
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dc.contributor.authorVictor O., Waziri-
dc.contributor.authorIsmaila, Idris-
dc.contributor.authorMohammed Bashir, Abdullahi-
dc.contributor.authorHakimi, Danladi-
dc.contributor.authorAudu, Isah-
dc.date.accessioned2021-07-14T14:16:06Z-
dc.date.available2021-07-14T14:16:06Z-
dc.date.issued2013-
dc.identifier.issn2221-0741-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/9390-
dc.description.abstractAiming to develop an immune based system, the negative selection algorithm aid in solving complex problems in spam detection. This is been achieve by distinguishing spam from non-spam (self from non-self). In this paper, we propose an optimized technique for e-mail classification. This is done by distinguishing the characteristics of self and non-self that is been acquired from trained data set. These extracted features of self and non-self are then combined to make a single detector, therefore reducing the false rate. (Non-self that were wrongly classified as self). The result that will be acquired in this paper will demonstrate the effectiveness of this technique in decreasing false rate.en_US
dc.language.isoenen_US
dc.publisherWorld of Computer Science and Information Technology Journal (WCSIT)en_US
dc.relation.ispartofseries;56-59-
dc.subjectNegative selection; E-mail Classification; Algorithm; Self, Non-Self, Artificial Immune System, Classification accuracy.en_US
dc.titleA Negative Selection Algorithm Based on Email Classification Techniquesen_US
dc.typeArticleen_US
Appears in Collections:Cyber Security Science

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