Machine learning for email spam filtering: review, approaches and open research problems

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popu...

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Vydané v:Heliyon Ročník 5; číslo 6; s. e01802
Hlavní autori: Dada, Emmanuel Gbenga, Bassi, Joseph Stephen, Chiroma, Haruna, Abdulhamid, Shafi'i Muhammad, Adetunmbi, Adebayo Olusola, Ajibuwa, Opeyemi Emmanuel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.06.2019
Elsevier
Predmet:
Analysis of algorithms
Computer privacy
Computer science
Computer security
Deep learning
e-mail
Internet
Machine learning
Naïve Bayes
Neural networks
Spam filtering
Support vector machines
surveys
systematic review
Computer science
Deep learning
Support vector machines
Naïve Bayes
Spam filtering
Neural networks
Machine learning
Analysis of algorithms
Computer security
Computer privacy
ISSN:2405-8440, 2405-8440
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Popis
Shrnutí:The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2019.e01802