Spam Detection Using Ensemble Learning

Gupta, Vashu; Mehta, Aman; Goel, Akshay; Dixit, Utkarsh; Pandey, Avinash Chandra

doi:10.1007/978-981-13-0761-4_63

Cited by 27 publications

(12 citation statements)

References 8 publications

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“…NSA-PSO hybrid scheme. [32] to detect textual-spam. In this method, "voting classifier" is used and comparison with different "supervised and unsupervised classifier" is done.…”

Section: Literature Surveymentioning

confidence: 99%

Email Spams via Text Mining using Machine Learning Techniques

Verma*,

Singh Gill

2020

IJITEE

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A lot of data is generated on daily basis which may potentially be useful. This data is generally unstructured and ambiguous to draw a meaning from it. High quality of information can be extracted from this potentially useful data typically through devising of patterns and trends in it. This is done using Text Mining which includes the initial parsing of the unstructured data, processing it and then leading to some meaningful and fascinating information hidden in it. This paper presents the machine learning techniques for text mining that are useful for spam detection in emails.

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“…NSA-PSO hybrid scheme. [32] to detect textual-spam. In this method, "voting classifier" is used and comparison with different "supervised and unsupervised classifier" is done.…”

Section: Literature Surveymentioning

confidence: 99%

Email Spams via Text Mining using Machine Learning Techniques

Verma*,

Singh Gill

2020

IJITEE

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“…In the combined approach, the ALO algorithm is utilized for the selection of features and Boosting approach is utilized for the classification procedure. Gupta et al [10] have considered the ensemble learning approach for the classification of email and SMS spam using the machine learning classifiers of the Decision Tree (DT), Bernoulli Naïve Bayes (BNB), Multinomial Naïve Bayes (MNB), and Gaussian Naïve Bayes (GNB). The authors have used the voting ensemble for the classification with different combinations of mentioned classifiers.…”

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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“…They are severely annoying to users (Almeida et al, 2011;Wang et al, 2010). An example of an annoying spam text message is as follows (Yamakami, 2003;Gupta et al, 2019;Chen et al, 2018)"CONGRATS: YOUR MOBILE NO HAVE WON YOU £500,000 IN ---MOBILE DRAW UK, TO CLAIM PRIZE SEND BANK DETAIL, NAME, ADDRESS, MOBILE NO, SEX, AGE, TO -. Such messages come not only from domestic but also international senders. A survey revealed that 68% of mobile phone users are affected by SMS Spam, with teenagers being the worst affected community 1 .…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, unlike emails that are supported with sophisticated spam filtering (Androutsopoulos et al, 2000;Drucker et al, 1999), SMS spam filtering is still not very robust. This is because most works that classify SMS spam (Chen et al, 2015(Chen et al, , 2018El-Alfy & AlHasan, 2016;Fu et al, 2014;Gupta et al, 2019;Kim et al, 2015;Osho et al, 2014) suffer from the limitation of manual feature engineering, which is not an efficient approach. Identifying prospective features for accurate classification requires prior knowledge and domain expertise.…”

Section: Introductionmentioning

confidence: 99%

Deep learning to filter SMS Spam

Roy

Singh

Banerjee

2020

Future Generation Computer Systems

164

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The popularity of short message service (SMS) has been growing over the last decade. For businesses, these text messages are more effective than even emails. This is because while 98% of mobile users read their SMS by the end of the day, about 80% of the emails remain unopened. The popularity of SMS has also given rise to SMS Spam, which refers to any irrelevant text messages delivered using mobile networks. They are severely annoying to users. Most existing research that has attempted to filter SMS Spam has relied on manually identified features. Extending the current literature, this paper uses deep learning to classify Spam and Not-Spam text messages. Specifically, Convolutional Neural Network and Long Short-term memory models were employed. The proposed models were based on text data only, and self-extracted the feature set. On a benchmark dataset consisting of 747 Spam and 4,827 Not-Spam text messages, a remarkable accuracy of 99.44% was achieved.

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Spam Detection Using Ensemble Learning

Cited by 27 publications

References 8 publications

Email Spams via Text Mining using Machine Learning Techniques

Email Spams via Text Mining using Machine Learning Techniques

Email Spam Detection using Ensemble Methods

Deep learning to filter SMS Spam

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