Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/96236
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Meli, Clyde | - |
dc.contributor.author | Nezval, Vitezslav | - |
dc.contributor.author | Oplatkova, Zuzana Kominkova | - |
dc.contributor.author | Buttigieg, Victor | - |
dc.date.accessioned | 2022-05-24T05:34:09Z | - |
dc.date.available | 2022-05-24T05:34:09Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Meli, C., Nezval, V., Kominkova Oplatkova, Z., & Buttigieg, V. (2017). Spam detection using linear genetic programming. 23rd International Conference on Soft Computing, Czech Republic. 80-92. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/96236 | - |
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 [“Reliable identification of bounded-length viruses is NP-complete” Inf. Theory IEEE Trans. On. 49, 1, 280–284 (2003).]‘s 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. [3]’s 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]. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Springer | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Spam filtering (Electronic mail) | en_GB |
dc.subject | Algorithms | en_GB |
dc.subject | Computer science -- Mathematics | en_GB |
dc.subject | Genetic programming (Computer science) | en_GB |
dc.subject | Linear programming | en_GB |
dc.subject | Electronic mail systems -- Security measures | en_GB |
dc.title | Spam detection using linear genetic programming | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | International Conference on Soft Computing | en_GB |
dc.bibliographicCitation.conferenceplace | Brno, Czech Republic, 20-22/06/2017 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - FacICTCIS |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Spam_detection_using_linear_genetic_programming(2017).pdf Restricted Access | 459.37 kB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.