Machine Learning Based Classification for Spam Detection

Serkan Keskin; Onur Sevli

Makale detayları

Machine Learning Based Classification for Spam Detection

Yazarlar:

Serkan Keskin | Onur Sevli

Yayıncı:

Sakarya University

Ana Başlıklar:

Artificial Intelligence

Dergi:

Sakarya University Journal of Science Vol.28 Issue 2, pp.270-282

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315401

pISSN:

2147-835X

Anahtar kelimeler:

Özet:

 Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the users inbox. In this study, Random Forest RF, Logistic Regression LR, Naive Bayes NB, Support Vector Machine SVM and Artificial Neural Network ANN algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

 Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the users inbox. In this study, Random Forest RF, Logistic Regression LR, Naive Bayes NB, Support Vector Machine SVM and Artificial Neural Network ANN algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

 Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the users inbox. In this study, Random Forest RF, Logistic Regression LR, Naive Bayes NB, Support Vector Machine SVM and Artificial Neural Network ANN algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

 Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the users inbox. In this study, Random Forest RF, Logistic Regression LR, Naive Bayes NB, Support Vector Machine SVM and Artificial Neural Network ANN algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

Makale detayları

Machine Learning Based Classification for Spam Detection

Yazarlar:

Serkan Keskin | Onur Sevli

Yayıncı:

Sakarya University

Ana Başlıklar:

Artificial Intelligence

Dergi:

Sakarya University Journal of Science Vol.28 Issue 2, pp.270-282

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315401

pISSN:

2147-835X

Artificial Intelligence

Email Classification

Machine Learning

Spam Detection

Sayı

Makale detayları

Machine Learning Based Classification for Spam Detection

Yazarlar: Serkan Keskin | Onur Sevli

Yayıncı: Sakarya University

Ana Başlıklar: Artificial Intelligence

Dergi: Sakarya University Journal of Science Vol.28 Issue 2, pp.270-282

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001315401

pISSN: 2147-835X

Artificial Intelligence

Email Classification

Machine Learning

Spam Detection

Sayı

Yazarlar:

Serkan Keskin | Onur Sevli

Yayıncı:

Sakarya University

Ana Başlıklar:

Artificial Intelligence

Dergi:

Sakarya University Journal of Science Vol.28 Issue 2, pp.270-282

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315401

pISSN:

2147-835X