Please use this identifier to cite or link to this item:
Title: Machine Learning for Email Spam Filtering: Review, Approaches, and Open Research Problems
Authors: Emmanuel, Dada
Adetunmbi, Adebayo
Chiroma, Haruna
Abdulhamid, Shafi’i Muhammad
Joseph, Stephen
Opeyemi, Ajibuwa
Keywords: Spam filtering
Naïve Bayes
Computer security
Computer privacy
Analysis of algorithms
Machine learning
Issue Date: 20-Apr-2019
Publisher: Heliyon
Series/Report no.: e01802;
Abstract: The upsurge in the volume of unwanted emails called spam has created an intense need for the development ofmore dependable and robust antispamfilters. Machine learning methods of recent are being used to successfullydetect andfilter spam emails. We present a systematic review of some of the popular machine learning basedemail spamfiltering approaches. Our review covers survey of the important concepts, attempts, efficiency, and theresearch trend in spamfiltering. The preliminary discussion in the study background examines the applications ofmachine learning techniques to the email spamfiltering process of the leading internet service providers (ISPs)like Gmail, Yahoo and Outlook emails spamfilters. Discussion on general email spamfiltering process, and thevarious 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 researchproblems in spamfiltering. We recommended deep leaning and deep adversarial learning as the future techniquesthat can effectively handle the menace of spam emails
Appears in Collections:Cyber Security Science

Files in This Item:
File Description SizeFormat 
5.pdfMachine learning for email spamfiltering: review, approaches and openresearch problems1.53 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.