Email Spam Detection Using Integrated Approach of Naïve Bayes and Particle Swarm Optimization

Agarwal, Kriti; Kumar, Tarun

doi:10.1109/iccons.2018.8662957

Cited by 54 publications

(21 citation statements)

References 3 publications

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“…Particle Swarm Optimisation was integrated along with Naïve Bayes. They concluded a success when their proposed integrated method increased the accuracy of the detection compared to NB alone [6].…”

Section: B Bio-inspired Methodsmentioning

confidence: 99%

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Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

et al. 2020

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Electronic mail has eased communication methods for many organisations as well as individuals. This method is exploited for fraudulent gain by spammers through sending unsolicited emails. This paper aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. An extensive research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed.

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Section: B Bio-inspired Methodsmentioning

confidence: 99%

“…Agarwal et al [6] experimented with NB along with Particle Swarm Optimisation (PSO) technique. The paper used the emails from Ling-Spam corpus and aimed to acquire an improvement in F1-score, Precision, Recall and Accuracy.…”

Section: B Bio-inspired Methodsmentioning

confidence: 99%

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

et al. 2020

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“…In the paper [5],authors investigated the use of string matching algorithms for spam email detection. Particularly this work examines and compares the efficiency of six well-…”

Section: Literature Surveymentioning

confidence: 99%

Email based Spam Detection

Sultana¹,

Sapnaz²,

Sana³

et al. 2020

IJERT

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Nowadays, a big part of people rely on available email or messages sent by the stranger. The possibility that anybody can leave an email or a message provides a golden opportunity for spammers to write spam message about our different interests .Spam fills inbox with number of ridiculous emails . Degrades our internet speed to a great extent .Steals useful information like our details on our contact list. Identifying these spammers and also the spam content can be a hot topic of research and laborious tasks. Email spam is an operation to send messages in bulk by mail .Since the expense of the spam is borne mostly by the recipient ,it is effectively postage due advertising. Spam email is a kind of commercial advertising which is economically viable because email could be a very cost effective medium for sender .With this proposed model the specified message can be stated as spam or not using Bayes' theorem and Naive Bayes' Classifier and Also IP addresses of the sender are often detected .

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“…Agarwal and Kumar [8] have amalgamated the concepts of Particle Swarm Optimization (PSO) and Naïve Bayes algorithm to detect the spam emails. In this approach, the PSO algorithm used to improve the classification efficacy evaluated using Naïve Bayes approach.…”

Section: Related Workmentioning

confidence: 99%

Email Spam Detection using Ensemble Methods

Bhardwaj¹,

Sharma²

2019

IJRTE

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The swiftly growth of spam email has escalated the need to upgrade the existing spam detection and filtration methods. There is the existence of several machine learning methods for the classification and detection of email spam but these lacks in some cases. In this research work ensemble methods are adapted to detect the email spam. The machine learning methods of Multinomial Naïve Bayes and J48 Decision Tree algorithms are considered and ensembled. The considered ensemble methods are bagging and boosting. The experimentation is conducted on the dataset of CSDMC2010 Spam corpus. The results for the considered dataset are evaluated using individual classifiers, bagging, and boosting ensemble approaches. The system performance is accessed in terms of precision, recall, f-measure, and accuracy. The experimental outcomes indicates the distinguish results for the detection of email spam using ensemble methods.

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Email Spam Detection Using Integrated Approach of Naïve Bayes and Particle Swarm Optimization

Cited by 54 publications

References 3 publications

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Email based Spam Detection

Email Spam Detection using Ensemble Methods

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