Email Spam Detection Using Machine Learning Algorithms

Kumar, Nikhil; Sonowal, Sanket; Nishant, .

doi:10.1109/icirca48905.2020.9183098

Cited by 70 publications

(27 citation statements)

References 4 publications

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“…e concept of boosting technique could be used for additional study in order to improve the system's outcomes. e authors in [1] used machine learning algorithms to detect spam emails. ey compiled a dataset using online tools such as 'kaggle' and others.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e Internet has become an inseparable part of human lives, where more than four and half billion Internet users find it a convenient to use it for their facilitation. Moreover, emails are considered as a reliable form of communication by the Internet users [1]. Over the decades, e-mail services have been evolved into a powerful tool for the exchange of different kind of information.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Detection of Spam Emails

Siddique

Khan

Din

et al. 2021

Scientific Programming

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“…The mentioned dataset has 4601 instances, 57 attributes, and a single output which allows classification of e-mail as spam or ham. A large group of machine-learning techniques for e-mail spam classification was also analyzed and presented in [18]. The authors studied the efficiency of the following algorithms: SVM, k-NN, NB, DT, RF, AdaBoost and Bagging.…”

Section: Related Workmentioning

confidence: 99%

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

2021

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The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mails as desirable (ham) or potentially harmful (spam) messages were compared in the paper to illustrate the operation of the meta-algorithm. The used methods are simple, but as the results showed, they are powerful enough. We use the following classifiers: k-nearest neighbours (k-NNs), support vector machines (SVM), and the naïve Bayes classifier (NB). The conducted research gave us the conclusion that multinomial naïve Bayes classifier can be an excellent weapon in the fight against the constantly increasing amount of spam messages. It was also confirmed that the proposed solution gives very accurate results.

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“…That target people of the spammers are those who are unaware of the spam contents. Thus, machine learning algorithms like Naïve Bayes, Support Vector Machine, K-Nearest neighbor and ensemble learning approaches like Random Forest Classifier and Bagging areapplied on the datasets which are used to detect spam emails and the algorithm that produces best result of spam detection with higher precision and accuracy is identified and selected for spam email detection [4].In [5], the IOT cyberspace is taken into account for detecting spams in IOT based social media applications. It is divided into 2 patterns, namely Behavior pattern and Semantic pattern-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

Rayan

Taloba

2021

Preprint

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An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanismthrough feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.

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Email Spam Detection Using Machine Learning Algorithms

Cited by 70 publications

References 4 publications

Machine Learning-Based Detection of Spam Emails

Machine Learning-Based Detection of Spam Emails

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

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