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Deep Learning Based Sentiment Analysis for Phishing SMS Detection

Deep Learning Based Sentiment Analysis for Phishing SMS Detection

Aakanksha Sharaff, Ramya Allenki, Rakhi Seth
Copyright: © 2021 |Pages: 28
ISBN13: 9781799880615|ISBN10: 1799880613|EISBN13: 9781799880639
DOI: 10.4018/978-1-7998-8061-5.ch001
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MLA

Sharaff, Aakanksha, et al. "Deep Learning Based Sentiment Analysis for Phishing SMS Detection." New Opportunities for Sentiment Analysis and Information Processing, edited by Aakanksha Sharaff, et al., IGI Global, 2021, pp. 1-28. https://doi.org/10.4018/978-1-7998-8061-5.ch001

APA

Sharaff, A., Allenki, R., & Seth, R. (2021). Deep Learning Based Sentiment Analysis for Phishing SMS Detection. In A. Sharaff, G. Sinha, & S. Bhatia (Eds.), New Opportunities for Sentiment Analysis and Information Processing (pp. 1-28). IGI Global. https://doi.org/10.4018/978-1-7998-8061-5.ch001

Chicago

Sharaff, Aakanksha, Ramya Allenki, and Rakhi Seth. "Deep Learning Based Sentiment Analysis for Phishing SMS Detection." In New Opportunities for Sentiment Analysis and Information Processing, edited by Aakanksha Sharaff, G. R. Sinha, and Surbhi Bhatia, 1-28. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-8061-5.ch001

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Abstract

Sentiment analysis works on the principle of categorizing and identifying the text-based content and the process of classifying documents into one of the predefined classes commonly known as text classification. Hackers deploy a strategy by sending malicious content as an advertisement link and attack the user system to gain information. For protecting the system from this type of phishing attack, one needs to classify the spam data. This chapter is based on a discussion and comparison of various classification models that are used for phishing SMS detection through sentiment analysis. In this chapter, SMS data is collected from Kaggle, which is classified as ham or spam; while implementing the deep learning techniques like Convolutional Neural Network (CNN), CNN with 7 layers, and CNN with 11 layers, different results are generated. For evaluating these results, different machine learning techniques are used as a baseline algorithm like Naive Bayes, Decision Trees, Support Vector Machine (SVM), and Artificial Neural Network (ANN). After evaluation, CNN showed the highest accuracy of 99.47% as a classification model.

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