Feature Selection Techniques for Email Spam Classification: A Survey

Vinitha, V. Sri; Renuka, D. Karthika

doi:10.1007/978-3-030-24051-6_86

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…In another study by Vinitha et al [12], different artificial neural network (ANN) models were evaluated, including feedforward neural networks (NN), multi-layer perceptron (MLP), recurrent neural networks (RNN), and long shortterm memory (LSTM) networks. The LSTM model exhibited high accuracy, reaching a rate of 97.4% in the investigation.…”

Section: Recent Research Has Focused On Understanding Attention Distr...mentioning

confidence: 99%

Deep Neural Network Based Spam Email Classification Using Attention Mechanisms

Ahmed,

Akter,

Rahman

et al. 2023

JILSA

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Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and inefficient. In our research, we employ deep neural networks like RNN, LSTM, and GRU, incorporating attention mechanisms such as Bahdanua, scaled dot product (SDP), and Luong scaled dot product self-attention for spam email filtering. We evaluate our approach on various datasets, including Trec spam, Enron spam emails, SMS spam collections, and the Ling spam dataset, which constitutes a substantial custom dataset. All these datasets are publicly available. For the Enron dataset, we attain an accuracy of 99.97% using LSTM with SDP self-attention. Our custom dataset exhibits the highest accuracy of 99.01% when employing GRU with SDP self-attention. The SMS spam collection dataset yields a peak accuracy of 99.61% with LSTM and SDP attention. Using the GRU (Gated Recurrent Unit) alongside Luong and SDP (Structured Self-Attention) attention mechanisms, the peak accuracy of 99.89% in the Ling spam dataset. For the Trec spam dataset, the most accurate results are achieved using Luong attention LSTM, with an accuracy rate of 99.01%. Our performance analyses consistently indicate that employing the scaled dot product attention mechanism in conjunction with gated recurrent neural networks (GRU) delivers the most effective results. In summary, our research underscores the efficacy of employing advanced deep learning techniques and attention mechanisms for spam email filtering, with remarkable accuracy across multiple datasets. This approach presents a promising solution to the ever-growing problem of spam emails.

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Section: Recent Research Has Focused On Understanding Attention Distr...mentioning

confidence: 99%

Deep Neural Network Based Spam Email Classification Using Attention Mechanisms

Ahmed,

Akter,

Rahman

et al. 2023

JILSA

View full text Add to dashboard Cite

show abstract

“…Feature selection is an essential phase in many classification problems, specially, in those with high dimensional datasets that may contain a large number of irrelevant, noisy and redundant features (Vinitha and Renuka 2020).…”

Section: Feature Selectionmentioning

confidence: 99%

A review of spam email detection: analysis of spammer strategies and the dataset shift problem

et al. 2022

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Spam emails have been traditionally seen as just annoying and unsolicited emails containing advertisements, but they increasingly include scams, malware or phishing. In order to ensure the security and integrity for the users, organisations and researchers aim to develop robust filters for spam email detection. Recently, most spam filters based on machine learning algorithms published in academic journals report very high performance, but users are still reporting a rising number of frauds and attacks via spam emails. Two main challenges can be found in this field: (a) it is a very dynamic environment prone to the dataset shift problem and (b) it suffers from the presence of an adversarial figure, i.e. the spammer. Unlike classical spam email reviews, this one is particularly focused on the problems that this constantly changing environment poses. Moreover, we analyse the different spammer strategies used for contaminating the emails, and we review the state-of-the-art techniques to develop filters based on machine learning. Finally, we empirically evaluate and present the consequences of ignoring the matter of dataset shift in this practical field. Experimental results show that this shift may lead to severe degradation in the estimated generalisation performance, with error rates reaching values up to $$48.81\%$$ 48.81 % .

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“…Paul Graham's Algorithm detected 99.5% spam with 0.03% false positives which he claimed a large dataset was the reason behind the 99.5% detection success. [9] in their work, different classifiers were used to determine whether an email is Ham or spam. They separated the dataset into two-part, one part for training and the other part for testing.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of LSTM and RNN Models in the Detection of Email Spam Messages

John-Africa

Emmah

2022

COMPUTE

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Email spam is an unwanted bulk message that is sent to a recipient’s email address without explicit consent from the recipient. This is usually considered a means of advertising and maximizing profit, especially with the increase in the usage of the internet for social networking, but can also be very frustrating and annoying to the recipients of these messages. Recent research has shown that about 14.7 billion spam messages are sent out every single day of which more than 45% of these messages are promotional sales content that the recipient did not specifically opt-in. This has gotten the attention of many researchers in the area of natural language processing. In this paper, we used the Long Short-Time Memory (LSTM) for classification tasks between spam and Ham messages. The performance of LSTM is compared with that of a Recurrent Neural Network( RNN) which can also be used for a classification task of this nature but suffers from short-time memory and tends to leave out important information from earlier time steps to later ones in terms of prediction. The evaluation of the result shows that LSTM achieved 97% accuracy with both Adams and RMSprop optimizers compared to RNN with an accuracy of 94% with RMSprop and 87% accuracy with Adams optimizer.

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Feature Selection Techniques for Email Spam Classification: A Survey

Cited by 7 publications

References 11 publications

Deep Neural Network Based Spam Email Classification Using Attention Mechanisms

Deep Neural Network Based Spam Email Classification Using Attention Mechanisms

A review of spam email detection: analysis of spammer strategies and the dataset shift problem

Performance Evaluation of LSTM and RNN Models in the Detection of Email Spam Messages

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