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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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| ArticleNumber | e01802 |
| Author | Bassi, Joseph Stephen Chiroma, Haruna Adetunmbi, Adebayo Olusola Abdulhamid, Shafi'i Muhammad Dada, Emmanuel Gbenga Ajibuwa, Opeyemi Emmanuel |
| AuthorAffiliation | a Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria b Department of Computer Science, Federal College of Education (Technical), Gombe, Nigeria e Department of Electrical Engineering, University of Ilorin, Ilorin, Nigeria c Department of Cyber Security Science, Federal University of Technology Minna, Minna, Nigeria d Department of Computer Science, Federal University of Technology Akure, Akure, Nigeria |
| AuthorAffiliation_xml | – name: e Department of Electrical Engineering, University of Ilorin, Ilorin, Nigeria – name: d Department of Computer Science, Federal University of Technology Akure, Akure, Nigeria – name: b Department of Computer Science, Federal College of Education (Technical), Gombe, Nigeria – name: c Department of Cyber Security Science, Federal University of Technology Minna, Minna, Nigeria – name: a Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria |
| Author_xml | – sequence: 1 givenname: Emmanuel Gbenga surname: Dada fullname: Dada, Emmanuel Gbenga email: gbengadada@unimaid.edu.ng organization: Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria – sequence: 2 givenname: Joseph Stephen surname: Bassi fullname: Bassi, Joseph Stephen organization: Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria – sequence: 3 givenname: Haruna surname: Chiroma fullname: Chiroma, Haruna organization: Department of Computer Science, Federal College of Education (Technical), Gombe, Nigeria – sequence: 4 givenname: Shafi'i Muhammad surname: Abdulhamid fullname: Abdulhamid, Shafi'i Muhammad organization: Department of Cyber Security Science, Federal University of Technology Minna, Minna, Nigeria – sequence: 5 givenname: Adebayo Olusola surname: Adetunmbi fullname: Adetunmbi, Adebayo Olusola organization: Department of Computer Science, Federal University of Technology Akure, Akure, Nigeria – sequence: 6 givenname: Opeyemi Emmanuel surname: Ajibuwa fullname: Ajibuwa, Opeyemi Emmanuel organization: Department of Electrical Engineering, University of Ilorin, Ilorin, Nigeria |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31211254$$D View this record in MEDLINE/PubMed |
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| Keywords | Computer science Deep learning Support vector machines Naïve Bayes Spam filtering Neural networks Machine learning Analysis of algorithms Computer security Computer privacy |
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| SubjectTerms | Analysis of algorithms Computer privacy Computer science Computer security Deep learning Internet Machine learning Naïve Bayes Neural networks Spam filtering Support vector machines surveys systematic review |
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