Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques
Spam remains a persistent issue that not only consumes time and bandwidth but also poses significant cybersecurity threats. As a result, effective spam filtering has become essential. With an emphasis on Naïve Bayes (NB), Decision Trees (DT), and Support Vector Machines (SVM), this study offers a th...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04013.pdf |
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| author | Zhang Chenwei |
| author_facet | Zhang Chenwei |
| author_sort | Zhang Chenwei |
| collection | DOAJ |
| description | Spam remains a persistent issue that not only consumes time and bandwidth but also poses significant cybersecurity threats. As a result, effective spam filtering has become essential. With an emphasis on Naïve Bayes (NB), Decision Trees (DT), and Support Vector Machines (SVM), this study offers a thorough analysis of the major machine learning techniques utilized in contemporary spam filtering. This paper investigates underlying principles of these methods, compares their performance through extensive experiments conducted on the Kaggle dataset, and discusses the cunent challenges and future directions for spam filtering technology. The study reveals that SVM is particularly effective for handling high-dimensional data. DT offers superior interpretability, and NB simplifies probabilistic classification. Experimental results demonstrate that while each method has its strengths and weaknesses, combining SVM with NB notably enhances classification accuracy. Despite these advances, spam filters still face challenges due to evolving spamming tactics. In order to address these persistent problems, the conclusion part highlights the need for more reliable and flexible spam filtering teclmologies and makes recommendations for future research directions. |
| format | Article |
| id | doaj-art-67e3295d112f4da9b4d573aed3f19ee8 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-67e3295d112f4da9b4d573aed3f19ee82025-02-07T08:21:13ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700401310.1051/itmconf/20257004013itmconf_dai2024_04013Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning TechniquesZhang Chenwei0Qingdao No.2 Middle SchoolSpam remains a persistent issue that not only consumes time and bandwidth but also poses significant cybersecurity threats. As a result, effective spam filtering has become essential. With an emphasis on Naïve Bayes (NB), Decision Trees (DT), and Support Vector Machines (SVM), this study offers a thorough analysis of the major machine learning techniques utilized in contemporary spam filtering. This paper investigates underlying principles of these methods, compares their performance through extensive experiments conducted on the Kaggle dataset, and discusses the cunent challenges and future directions for spam filtering technology. The study reveals that SVM is particularly effective for handling high-dimensional data. DT offers superior interpretability, and NB simplifies probabilistic classification. Experimental results demonstrate that while each method has its strengths and weaknesses, combining SVM with NB notably enhances classification accuracy. Despite these advances, spam filters still face challenges due to evolving spamming tactics. In order to address these persistent problems, the conclusion part highlights the need for more reliable and flexible spam filtering teclmologies and makes recommendations for future research directions.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04013.pdf |
| spellingShingle | Zhang Chenwei Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques ITM Web of Conferences |
| title | Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques |
| title_full | Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques |
| title_fullStr | Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques |
| title_full_unstemmed | Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques |
| title_short | Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques |
| title_sort | enhancing spam filtering a comparative study of modern advanced machine learning techniques |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04013.pdf |
| work_keys_str_mv | AT zhangchenwei enhancingspamfilteringacomparativestudyofmodernadvancedmachinelearningtechniques |