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Analysis Model at Sentence Level for Phishing Detection

Analysis Model at Sentence Level for Phishing Detection

Sabyasachi Pramanik
Copyright: © 2024 |Pages: 17
ISBN13: 9798369352717|ISBN13 Softcover: 9798369352724|EISBN13: 9798369352731
DOI: 10.4018/979-8-3693-5271-7.ch004
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MLA

Pramanik, Sabyasachi. "Analysis Model at Sentence Level for Phishing Detection." Machine Learning Techniques and Industry Applications, edited by Pramod Kumar Srivastava and Ashok Kumar Yadav, IGI Global, 2024, pp. 73-89. https://doi.org/10.4018/979-8-3693-5271-7.ch004

APA

Pramanik, S. (2024). Analysis Model at Sentence Level for Phishing Detection. In P. Srivastava & A. Yadav (Eds.), Machine Learning Techniques and Industry Applications (pp. 73-89). IGI Global. https://doi.org/10.4018/979-8-3693-5271-7.ch004

Chicago

Pramanik, Sabyasachi. "Analysis Model at Sentence Level for Phishing Detection." In Machine Learning Techniques and Industry Applications, edited by Pramod Kumar Srivastava and Ashok Kumar Yadav, 73-89. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-5271-7.ch004

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Abstract

Global cyber dangers related to phishing emails have increased dramatically, particularly after the COVID-19 epidemic broke out. Many companies have suffered significant financial losses as a result of this kind of assault. Even though many models have been developed to distinguish between phishing efforts and genuine emails, attackers always come up with new ways to trick their targets into falling for their scams. Many companies have suffered significant financial losses as a result of this kind of assault. Although phishing detection algorithms are being developed, their accuracy and speed in recognizing phishing emails are not up to par right now. Furthermore, the number of phished emails has concerningly increased lately. To lessen the negative effects of such bogus communications, there is an urgent need for more effective and high-performing phishing detection algorithms. Inside the framework of this study, a thorough examination of an email message's email header and content is carried out. A novel phishing detection model is built using the features of sentences that are extracted. The new dimension of sentence-level analysis is introduced by this model, which makes use of K Nearest Neighbor (KNN). Kaggle's well-known datasets were used to both train and evaluate the model. Important performance indicators, including as the F1-measure, precision, recall, and accuracy of 0.97, are used to assess the efficacy of this approach.

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