A Review on Various Techniques for Spam Detection

Kawale, Nitesh J.; Sait, Saad Yunus

doi:10.1109/icais50930.2021.9395979

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Kawale and Sait analyzed the whole network for evaluating spam [15]. For this, they acknowledged each network with weight and treated them as a node based on the review, functionality, and user behavior.…”

Section: Related Workmentioning

confidence: 99%

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Indu,

Dimri

2024

JWE

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Spam e-mails are continuously increasing and are a serious threats to a network and its users. Several efficient methods are available regarding this context, but still, it is evolving randomly. Considering this, the proposed approach addresses the problem of spam detection by combining traditional content-matching criteria with the modified version of the binomial logistic algorithm. The work generates seven categories for content-matching, which begins from three basic categories, namely: special words, adult content, and specific symbols and digits. The remaining four categories are derived from various possible combinations of these basic categories. The words selected for each category are carefully curated based on the human psychology of action and reaction. Then, a weight is assigned to each of the categories to signify their importance and a threshold criterion is deployed before implementing the binomial logistic algorithm, which not only increases the efficiency of the proposed algorithm but also reduces the rate of misclassification. The proposed model is tested on six separate datasets of Enron Spam Corpus, where 98.31% and 92.575% are the maximum and minimum accuracies achieved, respectively, in spam e-mail classification. The AUC_ROC scores for the entire Spam Corpus range between 0.927 and 0.983. A comparison is also carried out between the proposed algorithm and the other methods of spam detection that have logistic regression. Finally, the suggested method can adequately handle a large sample size without compromising the efficacy, which is measured using accuracy, precision, recall, F-measure, and AUC_ROC score.

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Section: Related Workmentioning

confidence: 99%

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Indu,

Dimri

2024

JWE

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Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison Study

Abdulrahman¹,

Salim

2022

ITM Web Conf.

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Emails have become the most economical and fastest communication forms. However, during the past few years, the increment of email users has dramatically increased spam emails. Various anti-spam techniques have been developed to minimize if not eliminate the spam problem. In this paper, we study the disparity in the effectiveness of using different decision tree algorithms in email classification and combat spam problems. For that, we have chosen Universiti Utara Malaysia emails as a case study. To achieve the best possible classification accuracy, we compared all chosen algorithms’ performance, which are Random Forest, LMT, Decision Stump, J48, Random Tree, and REP Tree. The experimental results showed that the Decision Stump algorithm is more effective to be used in classifying the emails, and the F-measures, Precision, and recall score for the Decision Stump algorithm are higher than the other comparison algorithms.

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A Privacy-Preserving Machine Learning Ensemble for Spam Detection

Upadhyaya

2023

2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA)

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A Review on Various Techniques for Spam Detection

Cited by 4 publications

References 15 publications

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison Study

A Privacy-Preserving Machine Learning Ensemble for Spam Detection

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