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

Dada, Emmanuel Gbenga; Bassi, Joseph Stephen; Chiroma, Haruna; Abdulhamid, Shafií Muhammad; Adetunmbi, Adebayo Olusola; Ajibuwa, Opeyemi Emmanuel

doi:10.1016/j.heliyon.2019.e01802

Cited by 396 publications

(203 citation statements)

References 59 publications

Supporting

Mentioning

201

Contrasting

Unclassified

Order By: Relevance

“…Dada et al [129] examined the applications of ML techniques to the email spam filtering process of leading internet service providers (ISPs), such as Gmail, Yahoo, and Outlook, and focused on revisiting the machine learning techniques used for filtering email spam over the 2004-2018 period, such as K-NN, NB, Neural Networks (NN), Rough set, SVM, NBTree classifiers, firefly algorithm (FA), C4.5/J48 decision tree algorithms, logistic model tree induction (LMT), and convolutional neural network (CNN). Stochastic optimization techniques, such as evolutionary algorithms (EAs), have also been explored by Dada et al [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).…”

Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

View full text Add to dashboard Cite

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

show abstract

Section: Email Miningmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

View full text Add to dashboard Cite

show abstract

“…The header is created (14) by combining the destination (15) and initial header information (16) to fill the IP address fields.…”

Section: )mentioning

confidence: 99%

Modeling Network Security: Case Study of Email System

Al-Fedaghi¹,

Alnasser²

2020

IJACSA

View full text Add to dashboard Cite

We study operational security in computer network security, including infrastructure, internal processes, resources, information, and physical environment. Current works on developing a security framework focus on a security ontology that contributes to applying common vocabulary, but such an approach does not assist in constructing a foundation for a holistic security methodology. We focus on defining the bounds and creating a representation of a security system by developing a diagrammatic representation (i.e. a model) as a means to describe computer network processes. The model, referred to a thinging machine, is a first step toward developing a security strategy and plan. The general aim is to demonstrate that the representation of the security system plays a key role in making thinking visible through conceptual description of the operational environment, a region in which active security operations are undertaken. We apply the proposed model for email security by conceptually describing a real email system.

show abstract

“…The work in [9] by Dada et al [10] concluded that there is a need to apply deep learning to spam filtering in order to exploit its numerous processing layers and many levels of abstraction to learn representations of data. The work in discusses the importance of traffic prediction in order to eliminate traffic redundancy in the cloud.…”

Section: Related Workmentioning

confidence: 99%

Modelling email traffic workloads with RNN and LSTM models

Boukoros

Nugaliyadde

et al. 2020

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

Analysis of time series data has been a challenging research subject for decades. Email traffic has recently been modelled as a time series function using a Recurrent Neural Network (RNN) and RNNs were shown to provide higher prediction accuracy than previous probabilistic models from the literature. Given the exponential rise of email workloads which need to be handled by email servers, in this paper we first present and discuss the literature on modelling email traffic. We then explain the advantages and limitations of different approaches as well as their points of agreement and disagreement. Finally, we present a comprehensive comparison between the performance of RNN and Long Short Term Memory (LSTM) models. Our experimental results demonstrate that both approaches can achieve high accuracy over four large datasets acquired from different universities' servers, outperforming existing work, and show that the use of LSTM and RNN is very promising for modelling email traffic.

show abstract

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

Cited by 396 publications

References 59 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Modeling Network Security: Case Study of Email System

Modelling email traffic workloads with RNN and LSTM models

Contact Info

Product

Resources

About