Year: 2024
Type: article
Abstract: Mobile phones and telecommunications networks have recently played an important role in modern society. They are dispensable parts of our lives as they facilitate the way we communicate. However, apart from their benefit, their proliferation has some drawbacks as telephone networks can be exploited. For example, commercial calls can be made repeatedly to advertise companies’ products. These calls annoy customers because they promote products without considering customers’ interests. These unexpected calls not only cause a negative impact on the networks but also disturb mobile phone users. To confront this problem, the network administrators need some methods to detect the phone numbers that are used to make the harassment. Therefore, we proposed a solution based on machine learning classification models. Then the performance of some models, namely K-Nearest Neighbors, Decision Tree, and Logistic Regression, is compared. By applying the machine learning models, network administrators can identify and restrict malicious telephone numbers.
Author: Van Vuong Ngo
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402929275