WITHDRAWN: Spam email detection using machine learning algorithm

Nayak, Rakesh; Jiwani, Salim Amirali; Rajitha, B.

doi:10.1016/j.matpr.2021.03.147

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…They tested their algorithm on three publicly available e-mail spam datasets and discovered that it outperformed the others in spam filtering. Nayak, Amirali Jiwani & Rajitha (2021) employed a hybrid strategy that combined Nave Bayes and Decision Tree algorithms to identify spam e-mails (DT). They were able to obtain an accuracy of 88.12% using their hybrid approach.…”

Section: Spam Text Classification Techniquesmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

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The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.

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Section: Spam Text Classification Techniquesmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

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“…However, they only use accuracy as the performance measure while using a single dataset which consists of self-collected emails. Nayak et al [20] employ NB and decision tree (J48) while using a single dataset from Kaggle. The performance metrics are accuracy, precision, recall, and F-score.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In particular, artificial intelligence (AI)-based methods have received notable attention from researchers in recent years. Special emphasis has been placed on the methods based on machine learning [1,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. In addition, deep learning methods have recently been successfully applied to spam email detection [10][11][12][13][14][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms

et al. 2022

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The increasing volume of unsolicited bulk emails has become a major threat to global security. While a significant amount of research has been carried out in terms of proposing new and better algorithms for email spam detection, relatively less attention has been given to evaluation metrics. Some widely used metrics include accuracy, recall, precision, and F-score. This paper proposes a new evaluation metric based on the concepts of fuzzy logic. The proposed metric, termed μO, combines accuracy, recall, and precision into a multi-criteria fuzzy function. Several possible evaluation rules are proposed. As proof of concept, a preliminary empirical analysis of the proposed scheme is carried out using two models, namely BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long short-term memory) from the domain of deep learning, while utilizing three benchmark datasets. Results indicate that for the Enron and PU datasets, LSTM produces better results of μO, with the values in the range of 0.88 to 0.96, whereas BERT generates better values of μO in the range of 0.94 to 0.96 for Lingspam dataset. Furthermore, extrinsic evaluation confirms the effectiveness of the proposed fuzzy logic metric.

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“…Sarker et al (16) performed an effectiveness analysis of machine learning security modeling with optimal features on a broad scale. Nayak et al (10) proposed a method for spam email detection, which employs a hybrid bagging approach as feature and combined the Naive Bayes and Decision Tree machine learning algorithms as classifiers which achieve overall 88.12% of accuracy. Sheu et al (11) proposed a method concentrating on email header analysis using a decision tree classifier to search for spam association guidelines at first.…”

Section: Related Workmentioning

confidence: 99%

Content-based Spam Email Detection Using N-gram Machine Learning Approach

Euna¹,

Hossain²,

Anwar³

et al. 2021

Preprint

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Recently, spam emails have become a significant problem with the expanding usage of the Internet. It is to some extend obvious to filter emails. A spam filter is a system that detects undesired and malicious emails and blocks them from getting into the users' inboxes. Spam filters check emails for something "suspicious" in terms of text, email address, header, attachments, and language. However, we have used different features such as word2vec, word n-grams, character n-grams, and a combination of variable length n-grams for comparative analysis in our proposed approach. Different machine learning models such as support vector machine (SVM), decision tree (DT), logistic regression (LR), and multinomial naïve bayes (MNB) are applied to train the extracted features. We use different evaluation metrics such as precision, recall, f1-score, and accuracy to evaluate the experimental results. Among them, SVM provides 97.6 \% of accuracy, 98.8\% of precision, and 94.9\% of f1-score using a combination of n-gram features.

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WITHDRAWN: Spam email detection using machine learning algorithm

Cited by 19 publications

References 9 publications

A systematic literature review on spam content detection and classification

A systematic literature review on spam content detection and classification

A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms

Content-based Spam Email Detection Using N-gram Machine Learning Approach

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