A Web application to detect phishing links using machine learning approach

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dc.contributor.authorKimani, B. M.
dc.date.accessioned2023-10-06T07:59:58Z
dc.date.available2023-10-06T07:59:58Z
dc.date.issued2023
dc.descriptionFull- text thesis
dc.description.abstractCovid-19 fast tracked the transition of ways of working and learning into remote working models and with it the rise of Cyber Crime. Phishing is one of the most effective cybercrimes where attackers deceive users into falling for baits that enable them to steal personal information including identification or social security number, password, credit card details, debit card details and usernames which they then use to commit crimes mostly associated with financial losses and or identity thefts. These attacks are frequently carried out through malicious emails, texts, and phone calls. Even in the presence of robust antivirus software and phishing detection tools, phishing has continued to be rampant and widespread as adoption of IT across the world goes up. Several methods have been implemented to deal with phishing attacks including, blacklisting phishing Uniform Resource Locators (URLs), heuristic rule based and machine learning models. The blacklist anti-phishing approach is limited in its ability to detect new phishing URLs due to its reliance on a pre-compiled list of phishing URLs, whereas most Machine Learning (ML) methods for detecting phishing websites have been reported with very decent detection accuracy but significant false alarm rates. Attackers are continuously re-inventing methods of attacks which as well makes heuristic rule-based methods vulnerable to failed detection. This study has provided further information on phishing attacks to create awareness, designed and implemented a web-based phishing links detection tool using ML deep learning model which achieved an accuracy of 94.27 complemented by heuristic rules and blacklist capabilities. Further improvement areas including continuous models training and features re-engineering for improved prediction accuracy, and equipping Internet of Things (IoT) gadgets with developed Application Programming Interface (API’s) capability to determine what endpoints to or not to interact with. Keywords: Remote working, Phishing, Identity thefts, Machine Learning, Detection tool.
dc.identifier.citationKimani, B. M. (2023). A Web application to detect phishing links using machine learning approach [Strathmore University]. http://hdl.handle.net/11071/13531
dc.identifier.urihttp://hdl.handle.net/11071/13531
dc.language.isoen
dc.publisherStrathmore University
dc.titleA Web application to detect phishing links using machine learning approach
dc.typeThesis
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MSIT Theses and Dissertations (2023)