Work

Malsmsdetector: malicious text message detector with hybrid feature vector and stacked ensemble model: a comparative study

Year: 2025

Type: article

Abstract: In recent years, the emergence of telecommunication systems has led to an increase in global electronic messaging traffic. Most of this traffic contains unwanted content for the user. In this study, an approach is proposed in which feature vectors generated using DBOW and PV-DM techniques are used for classification as a hybrid for spam SMS detection. In the training and testing of the proposed method, four different datasets (UCI, BEC, Big NUS and DITNUS) that are widely used are combined and used. This dataset is tested with 10 different machine learning algorithms and then a unique stacked ensemble model is proposed to increase the performance. In the tests using the model, accuracy, precision, recall, F-score and AUC values are 98.38%, 98.39%, 98.39%, 98.37% and 96.81%, respectively. When 10-fold cross validation is applied to the obtained results, the standard deviation value is 0.004. The analysis time per sample is 0.087 milliseconds.

Source: Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

DOI: 10.28948/ngumuh.1563906

Author: Recep Sinan Arslan

Institution: Kayseri Üniversitesi

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Kayseri Üniversitesi

Award: G1854050047