Diploma Theses for the Academic Year 2024-2025

Brief Description:

This thesis focuses on the use of computational intelligence techniques to detect and address spam emails. The uncontrolled sending of spam messages is a significant problem for both users and the infrastructure of email service providers. The purpose of this thesis is to examine and compare modern artificial intelligence methods, such as machine learning, neural networks, and evolutionary algorithms, for the effective detection and filtering of spam. Initially, the basic principles of spam emails and the characteristics that distinguish them from regular emails will be presented. Then, classical filtering techniques, such as rule-based filters and statistical models, will be analysed. This thesis will primarily focus on the use of more advanced techniques, such as neural networks (MLP, CNN, LSTM), classification algorithms (SVM, Naive Bayes), and evolutionary algorithms (PSO, SCA), and will examine how they can improve the accuracy and speed of the filtering process. Furthermore, an analysis of the advantages and disadvantages of each approach will be conducted, and the challenges concerning the ongoing evolution of spam messages and the need for filters to adapt to new spamming techniques will be discussed. This thesis will also include experimental results from the application of these techniques, demonstrating their effectiveness in practice. Finally, the potential for improving the respective algorithms will be presented, as well as the possibility of creating new, more advanced, and adaptive algorithms to counter the threats posed by spammers. Additionally, a Gmail account will be used to implement the codes of the aforementioned methods in order to evaluate their performance on real data.

Required background knowledge: Linux, Docker, Python, AI, Machine Learning, Neural Networks

Place: ZB203 – Ερευνητικό Εργαστήριο TelSiP

Supervisor: Dr. Grigorios Koulouras, Associate Professor

Student: Konstantina Pano