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

By Emmanuel Gbenga Dada, Joseph Stephen Bassi, Haruna Chiroma, Shafii Muhammad Abdulhamid, Adebayo Olusola Adetunmbi, Opeyemi Ajibuwa. Heliyon, 2019

475 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/5421c4a23574f3b46a5ad7a46728a210bd78581f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yi Tay
2. Donald Metzler
3. Vinh Q. Tran
4. Jai Gupta
5. Lucy Vasserman
6. Hossein Hassani
7. Jeffrey Scott Sorensen
8. Maedeh Taj Mazinani

In Computer Networks and Communications

1. Ernesto Damiani
2. Zhibo Zhang
3. Fatma Taher
4. Chan Yeob Yeun
5. Hussam Al Hamadi

In Information Systems

1. Hossein Hassani
2. Jeffrey Scott Sorensen
3. Yi Tay
4. Lucy Vasserman
5. Maedeh Taj Mazinani
6. Stephane Unger
7. Donald Metzler
8. Christina Beneki

Citations in the corpus

• Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “2) SPAM Due to the increasing number of Internet users, spam has become a major problem for Internet users in recent years [163].”
• Text Mining in Big Data Analytics (2020, methodology): “[129], as the optimization engines are able to enhance feature selection strategies within the anti-spam methods, such as the genetic algorithm (GA), particle swarm optimization (PSO), and ant colony algorithm (ACO).”
• A New Generation of Perspective API: Efficient Multilingual Character-level Transformers (2022, methodology): “, email spam [6]), research using direct text classification started in earnest with the introduction of the modest sized hand labeled data in [8], [22] and [32].”