Analysis of Machine Learning Algorithms by Developing a Phishing Email and Website Detection Model
5th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1741145
ABSTRACT
Machine Learning is a key branch of Artificial Intelligence that concentrates on the development of computational algorithms by creating models. It has caught major attention in the technological domain due to its various applications in speech recognition, recommendation engines, computer vision, automated stock trading etc. The model's performance is dependent on the dataset provided and its accuracy can easily be enhanced by expanding the training dataset. Post Covid-19, it has been observed that phishing websites are appallingly on the rise, especially the phishing attacks. These attacks are caused by cybercriminals using PDF's, Microsoft office documents and other attachments via emails. This paper focusses on discussion and comparison of different machine learning algorithms that are capable of detecting phishing emails and websites. The experiments have shown that that MultinomialNB attains the highest efficiency of 98.06% for phishing email detection and Decision Tree Classifier offers the maximum efficiency of 95.41% for phishing website detection. © 2021 IEEE.
Artificial Intelligence; Classification; Decision Tree; Logistic Regression; Machine Learning; Naive Bayes; Phishing detection; Random Forest; Sentiment Analysis; Computer crime; Efficiency; Electronic mail; Learning algorithms; Speech recognition; Websites; Detection models; Logistics regressions; Machine learning algorithms; Machine-learning; Phishing; Phishing detections; Phishing websites; Random forests; Decision trees
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2021
Year:
2021
Document Type:
Article
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