Ensemble Approach to Classify Spam SMS from Bengali Text

Al Maruf, Abdullah; Al Numan, Abdullah; Haque, Md. Mahmudul; Jidney, Tasmia Tahmida; Aung, Zeyar

doi:10.1007/978-3-031-37940-6_36

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…SMS is a text messaging platform that allows users to exchange short text messages using standardized communication protocols, [7]. These messages are succinct, filled with abbreviations, and less formal, posing a challenge in spam classification.…”

Section: Problem Statementmentioning

confidence: 99%

SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing

Oyeyemi,

Ojo

2023

JAMCS

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In the modern era, mobile phones have become ubiquitous, and Short Message Service (SMS) has grown to become a multi-million-dollar service due to the widespread adoption of mobile devices and the millions of people who use SMS daily. However, SMS spam has also become a pervasive problem that endangers users' privacy and security through phishing and fraud. Despite numerous spam filtering techniques, there is still a need for a more effective solution to address this problem [1]. This research addresses the pervasive issue of SMS spam, which poses threats to users' privacy and security. Despite existing spam filtering techniques, the high false-positive rate persists as a challenge. The study introduces a novel approach utilizing Natural Language Processing (NLP) and machine learning models, particularly BERT (Bidirectional Encoder Representations from Transformers), for SMS spam detection and classification. Data preprocessing techniques, such as stop word removal and tokenization, are applied, along with feature extraction using BERT. Machine learning models, including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random Forest, are integrated with BERT for differentiating spam from ham messages. Evaluation results revealed that the Naïve Bayes classifier + BERT model achieves the highest accuracy at 97.31% with the fastest execution time of 0.3 seconds on the test dataset. This approach demonstrates a notable enhancement in spam detection efficiency and a low false-positive rate. The developed model presents a valuable solution to combat SMS spam, ensuring faster and more accurate detection. This model not only safeguards users' privacy but also assists network providers in effectively identifying and blocking SMS spam messages.

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Section: Problem Statementmentioning

confidence: 99%

SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing

Oyeyemi,

Ojo

2023

JAMCS

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“…Criminals may also exploit holidays and major events. While there have been several studies on filtering and classifying spam messages in English (Salman et al, 2024), limited research has been conducted on spam detection in Turkish like Bengali (Al Maruf et al, 2023), and Indonesia (Theodorus et al, 2021). This study aims to fill this gap by classifying Turkish SMS messages as either spam or normal messages.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of learning techniques in the context of Turkish spam detection

Şengel

2024

Batman Üniversitesi Yaşam Bilimleri Dergisi

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Short Message Service (SMS) is a mobile messaging tool used by billions of people to communicate via a mobile phone. However, due to the lack of proper message filtering techniques, this form of communication is vulnerable to unwanted and junk messages. This paper compared SMS spam detection approaches based on machine learning methods such as Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Multinominal Naïve Bayes (MNB), Logistic Regression (LR), and Support Vector Machines (SVM) and deep learning methods such as Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Long Short Term Memory (LSTM) in terms of f-score, accuracy, recall, precision, and a confusion matrix constructed for each strategy. The study tested two different preprocessing methods on two different Turkish SMS datasets to evaluate the approaches. The aim of this study is to contribute to the issue of spam filtering in Turkey. The results indicate that the highest accuracy values were achieved with Support Vector Machine (99.03%) using the first preprocessing method and Logistic Regression and Random Forest (98.07%) using the second preprocessing method on the BigTurkishSMS dataset, a combination of the two datasets used. As is the case with the majority of machine learning algorithms, the second preprocessing of the data set yielded superior results in deep learning models. The ANN model achieved the highest accuracy, with a score of 97.41%. The study employed a comparison of machine learning and deep learning techniques on Turkish SMS datasets, which will provide valuable insights for researchers working in this field.

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