Spam Mail Detection Using Artificial Neural Network and Bayesian Filter

Özgür, Levent; Güngör, Tunga; Gürgen, Fikret

doi:10.1007/978-3-540-28651-6_74

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…Neural Network has positively contributed when it comes to discovering spam emails. The effort made by Ozgur et al [44], confirmed that claim. Nonetheless, the experimental outcomes were not satisfactory.…”

Section: Intelligent Spam Classification Approachesmentioning

confidence: 80%

Section: Experimental Settingmentioning

confidence: 99%

“…This algorithm is selected due to the fact it is inherently learn incrementally. In addition, it has been frequently employed in creating classification models and particularly in classifying spam emails in particular [33,42,44] which is the domain under investigation through this article. Considering that BL may work with no incremental algorithm we also used “Support Vector Machine” (SVM) which has also been employed in predicting spam emails as well as several other domains [78,79,81].…”

Section: Evaluating the Elcadpmentioning

confidence: 99%

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A lifelong spam emails classification model

Mohammad

2020

ACI

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Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

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Section: Intelligent Spam Classification Approachesmentioning

confidence: 80%

Section: Experimental Settingmentioning

confidence: 99%