A feature-centric spam email detection model using diverse supervised machine learning algorithms

Zamir, Ammara; Khan, Hikmat Ullah; Mehmood, Waqar; Iqbal, Tassawar; Akram, Abubakker Usman

doi:10.1108/el-07-2019-0181

Cited by 25 publications

(18 citation statements)

References 35 publications

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“…They used SVM and ANN models for the classification. Ammara and hikmat [18] has done the study using several machine learning and deep learning models. SVM, AdaBoost, MLP, DNN, RF models are used in the research.…”

Section: Related Workmentioning

confidence: 99%

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Islam¹,

Amin²,

Islam³

et al. 2021

Preprint

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Communication through email plays an essential part especially in every sector of our day-to-day life. Considering its significance, it is important to filter spam emails from emails. Spam email, also known as junk email, is unwanted messages that are sent by the electronic medium in large quantities. Most of the spam emails are commercial in nature that is not only irritating but also harmful due to malicious scams or malware-hosting sites or use viruses attached to the message. In this paper, we identify spam emails and expose how spam emails can be distinguished from legitimate/normal emails. We deployed four machine learning models and two deep learning models over the datasets including the combined dataset. Besides, we also try to find the important keywords that are found repeatedly from spam emails repository. This type of knowledge will enable us to detect spam emails for our personnel and community security purpose.<br>

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Section: Related Workmentioning

confidence: 99%

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Islam¹,

Amin²,

Islam³

et al. 2021

Preprint

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“…One of the deep learning models is long shortterm memory (LSTM). e LSTM architecture is a recurrent neural network (RNN) [25]. It is made up of feedback links and can handle both entire data sequences and single data points [26].…”

Section: Lstmmentioning

confidence: 99%

Machine Learning-Based Detection of Spam Emails

Siddique

Khan

Din

et al. 2021

Scientific Programming

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Social communication has evolved, with e-mail still being one of the most common communication means, used for both formal and informal ways. With many languages being digitized for the electronic world, the use of English is still abundant. However, various native languages of different regions are emerging gradually. The Urdu language, coming from South Asia, mostly Pakistan, is also getting its pace as a medium for communications used in social media platforms, websites, and emails. With the increased usage of emails, Urdu’s number and variety of spam content also increase. Spam emails are inappropriate and unwanted messages usually sent to breach security. These spam emails include phishing URLs, advertisements, commercial segments, and a large number of indiscriminate recipients. Thus, such content is always a hazard for the user, and many studies have taken place to detect such spam content. However, there is a dire need to detect spam emails, which have content written in Urdu language. The proposed study utilizes the existing machine learning algorithms including Naive Bayes, CNN, SVM, and LSTM to detect and categorize e-mail content. According to our findings, the LSTM model outperforms other models with a highest score of 98.4% accuracy.

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“…To combat the problems, various scientific research studies have been conducted, including the application of machine learning [11]. Previous scientific studies were categorized into three approaches, single-based machine learning, hybrid, and feature engineering [12].…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the existing models failed to consider the number of features in high-dimensional datasets, leading to high time complexities. Nevertheless, the finding by Majeed [20] shows that time complexity is an important factor to be considered in model development since it reduces the training speed and decreases the importance of the model to be used in online spam filtering [11]. Time complexity depends on the number of features required in a given model as well as whether the proposed method is linear or nonlinear [21].…”

Section: Introductionmentioning

confidence: 99%

Low Time Complexity Model for Email Spam Detection using Logistic Regression

Mrisho¹,

Ndibwile²,

Sam³

2021

IJACSA

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Spam emails have recently become a concern on the Internet. Machine learning techniques such as Neural Networks, Naïve Bayes, and Decision Trees have frequently been used to combat these spam emails. Despite their efficiency, time complexity in high-dimensional datasets remains a significant challenge. Due to a large number of features in high-dimensional datasets, the intricacy of this problem grows exponentially. The existing approaches suffer from a computational burden when thousands of features are used (high-time complexity). To reduce time complexity and improve accuracy in high-dimensional datasets, extra steps of feature selection and parameter tuning are necessary. This work recommends the use of a hybrid logistic regression model with a feature selection approach and parameter tuning that could effectively handle a big dimensional dataset. The model employs the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction method to mitigate the drawbacks of Term Frequency (TF) to obtain an equal feature weight. Using publicly available datasets (Enron and Lingspam), we compared the model's performance to that of other contemporary models. The proposed model achieved a low level of time complexity while maintaining a high level of spam detection rate of 99.1%.

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A feature-centric spam email detection model using diverse supervised machine learning algorithms

Cited by 25 publications

References 35 publications

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Spam-Detection with Comparative Analysis and Spamming Words Extractions

Machine Learning-Based Detection of Spam Emails

Low Time Complexity Model for Email Spam Detection using Logistic Regression

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