Publicação
Artificial Intelligence Models to Predict Malicious Network Traffic
| Resumo: | This research explores how Artificial Intelligence models can predict malicious network traffic. This is relevant with the increasing number of cyberattacks, considering Artificial Intelligence technology has the ability to protect against them. To do so, it is important to first determine which models have are able to play this defensive role. The focus and objective of this research is to understand the practical use and the predictive power of these models and, with the power of Python, an experiment is conducted to assess whether there is a model that is able to predict malicious network according to several metrics such as accuracy, support, recall, and F1 score considering a public dataset found on Kaggle. The research proves that Artificial Intelligence, and especially Machine Learning models, have the potential to help organizations stay cybersafe against attacks, provided the models are used by professionals who are able to understand not only the models, but also the business in which they are inserted. |
|---|---|
| Autores principais: | Barros, Sara Antunes |
| Assunto: | Artificial Intelligence Cybersecurity Machine Learning Predictive Modelling SDG 9 - Industry, innovation and infrastructure |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This research explores how Artificial Intelligence models can predict malicious network traffic. This is relevant with the increasing number of cyberattacks, considering Artificial Intelligence technology has the ability to protect against them. To do so, it is important to first determine which models have are able to play this defensive role. The focus and objective of this research is to understand the practical use and the predictive power of these models and, with the power of Python, an experiment is conducted to assess whether there is a model that is able to predict malicious network according to several metrics such as accuracy, support, recall, and F1 score considering a public dataset found on Kaggle. The research proves that Artificial Intelligence, and especially Machine Learning models, have the potential to help organizations stay cybersafe against attacks, provided the models are used by professionals who are able to understand not only the models, but also the business in which they are inserted. |
|---|