Background: Phishing is one type of the social engineering techniques to fool users by pretending tobe a trusted person and stealing users personal data. Quite often, Phishing spreads to email services, and browsers are not always able to block Phishing URLs. The problem of Phishing continues to exist and does not decrease, so there are still issues in this problem that need to be addressed. Objectives: The object of research is the method of processing and detecting Phishing URLs. This study is intended to conduct a study to identify the possible assumptions for the method of automating the processing and detection of Phishing URLs, as well as to find out how the efficiency can be improved, and the detection of Phishing URLs, in addition, this study is also intended to understand which of machine learning algorithms are best suited for detecting Phishing URLs. Methods: In this study, the method of machine learning is used, a study was also carried out, on the basis of which it was decided that these data are not enough and that a better result could be achieved if more efficient methods were used. Therefore, in this case, it was decided to use the machine learning method, and aquantitative study was carried out to understand which machine learning algorithm is better to use in furtherwork.The subject of research - methods and means of processing and detecting Phishing URLs. Also, the research methods in this study, is analysis, observation, modeling, and experimental research Results: The result shows a higher percentage compared to the algorithm comparison. Also, the result shows that the automation procedure has been achieved, and the accuracy of Phishing URL detection hasimproved a lot, showing an accuracy of 98.417%. Compared to manual analysis of Phishing URLs, and otheralgorithms, this is a better result. Conclusions: There are some challenges in handling Phishing URLs as well as efficiency and betterdetection. However, further research is needed in this case to find out how to further improve the detection of Phishing URLs.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-23360 |
| Date | January 2022 |
| Creators | Yaitskyi, Andrii |
| Publisher | Blekinge Tekniska Högskola |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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