A Master’s Thesis on Detecting Scam Websites Using Group Categorisation

/ / University-News

AMMAN – Researcher and Faculty of Information Technology student Yusra Majed al-Sharaf defended her Master’s thesis, titled [detecting scam websites using group categorisation].

The researcher investigated ways to deal with the different types of online scams, and presented a clear definition of these attacks as she deemed her efforts necessary due to the increasing number of online users.

To achieve her objective, the researcher analysed a three-model group category to detect scam websites and online attacks. She stressed the importance of raising users’ awareness of these websites and how to prevent such attacks, and suggested countermeasures for each type of scam depending on the analysed content.

The researcher found that the basis for countering these attacks lies within developing anti-scam technologies, specifically software used to set up website security. She also perceived that the algorithm for detecting scam websites using three group categorisations is sufficient in providing the necessary protection against these attacks.

The suggested algorithm scored a success rate of 98.52%, which is higher than the Random Forest Algorithm by 1.26%, and the Support Vector Machine by 3.16%, and the Decision Tree Algorithm by 2.65%.

The thesis assessment committee presiding over this project included Dr. Hesham Abusaimeh, as Ms. al-Sharaf’s supervisor, Dr. Muthafar al-Jarrah, as the internal MEU evaluator and head of the committee, and Dr. Mohammad al-Shkokani from the Applied Science Private University, as the external evaluator.