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Journal articles on the topic 'Spam SMS'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Prachi, Ms Akshara, Mrs Ankita Gandhi, Mr Garv Kavdia, and Mr Deepak Parmar. "Spam SMS Classification." International Journal of Research Publication and Reviews 6, no. 3 (2025): 5956–61. https://doi.org/10.55248/gengpi.6.0325.12109.

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Mehta, Riya, and Ankita Gandhi. "A Survey: SMS Spam Filtering." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 2672–77. http://dx.doi.org/10.31142/ijtsrd12850.

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Suprihati, Ferin Reviantika. "Analisis Klasifikasi SMS Spam Menggunakan Logistic Regression." Jurnal Sistem Cerdas 4, no. 3 (2021): 155–60. http://dx.doi.org/10.37396/jsc.v4i3.166.

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SMS or Short Message Service is usually found on cell phones. SMS is divided into two categories, namely SMS spam and SMS non-spam (ham). Spam SMS is an SMS that is annoying to phone users because it tends to contain messages that are not important such as promos and scams. Meanwhile, non-spam SMS (ham) tend to contain important SMS, such as messages from previous users. In this study, the classification of spam SMS and non-spam SMS (ham) was carried out using the logistic regression method. The purpose of this study is to distinguish or classify between spam and non-spam SMS (ham). The datase

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Jaya Saputra, Nesan. "Analysis of SMS Spam Detection using Tf-Idf: A Study On SMS Spam Collection Dataset." Jurnal Sosial Teknologi 4, no. 4 (2024): 213–17. http://dx.doi.org/10.59188/jurnalsostech.v4i4.1214.

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This study explores the detection of SMS spam utilizing TF-IDF analysis on a dataset containing a collection of text messages labeled as spam or ham (non-spam). The dataset comprises messages suitable for spam detection analysis using TF-IDF techniques. The research aims to evaluate the effectiveness of TF-IDF in distinguishing between spam and spam (non-spam) messages. The analysis involves examining the precision, recall, and F1-score metrics to assess the performance of the classification model. The results demonstrate promising outcomes, with a high accuracy rate achieved in classifying sp

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Riya, Mehta, and Gandhi Ankita. "A Survey SMS Spam Filtering." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 2672–77. https://doi.org/10.31142/ijtsrd12850.

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Now a days Short Message Service SMS is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service SMS spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques a

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Choudhary, Esha. "Spam SMS Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6868–76. http://dx.doi.org/10.22214/ijraset.2023.53235.

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Abstract: As the popularity of mobile phone devices has increased, Short Message Service (SMS) has grown into a multi-billion dollars industry. At the same time, reduction in the cost of messaging services has resulted in growth in unsolicited commercial advertisements (spams) being sent to mobile phones. In parts of Asia, up to 30% of text messages were spam in 2012. Lack of real databases for SMS spams, short length of messages and limited features, and their informal language are the factors that may cause the established email filtering algorithms to underperform in their classification. I

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Ma, Jialin, Yongjun Zhang, Zhijian Wang, and Kun Yu. "A Message Topic Model for Multi-Grain SMS Spam Filtering." International Journal of Technology and Human Interaction 12, no. 2 (2016): 83–95. http://dx.doi.org/10.4018/ijthi.2016040107.

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At present, content-based methods are regard as the more effective in the task of Short Message Service (SMS) spam filtering. However, they usually use traditional text classification technologies, which are more suitable to deal with normal long texts; therefore, it often faces some serious challenges, such as the sparse data problem and noise data in the SMS message. In addition, the existing SMS spam filtering methods usually consider the SMS spam task as a binary-class problem, which could not provide for different categories for multi-grain SMS spam filtering. In this paper, the authors p

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Hameed, Sarab M. "Differential evolution detection models for SMS spam." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 596. http://dx.doi.org/10.11591/ijece.v11i1.pp596-601.

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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental

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Sarab, M. Hameed. "Differential evolution detection models for SMS spam." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 596–601. https://doi.org/10.11591/ijece.v11i1.pp596-601.

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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental

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Liu, Xiaoxu, Haoye Lu, and Amiya Nayak. "A Spam Transformer Model for SMS Spam Detection." IEEE Access 9 (2021): 80253–63. http://dx.doi.org/10.1109/access.2021.3081479.

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Bhatia, Shailee, and . "A Study on Spam Detection Methods for Safe SMS Communication." International Journal of Engineering & Technology 7, no. 3.12 (2018): 790. http://dx.doi.org/10.14419/ijet.v7i3.12.16502.

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The electronic communication enables the instant and all type availability of user. The different form of information transition can be drawn in the form of SMS and emails. But these emails and SMS systems are also used by the individuals and firm as medium of their advertisement. Spam messages not only involves the unwanted messages but it also includes some viruses and threat to the security system. In this paper, a study to the SMS filtration methods is provided. The paper has explored the types of SMS spams, its threats and various filtration methods to detect the spam SMS.

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Arif Sofyan, Mohamad, Nining Rahaningsih, and Raditya Danar Dana. "DETEKSI SMS SPAM BERBAHASA INDONESIA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 3 (2024): 3071–79. http://dx.doi.org/10.36040/jati.v8i3.9532.

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Setiap individu membutuhkan akses informasi untuk memperluas pengetahuan mereka tentang berbagai hal. Salah satu metode yang populer dalam mengalirkan informasi adalah melalui layanan Short Message Service (SMS), Namun, penggunaan SMS dapat menimbulkan masalah dengan munculnya SMS spam (Sending and Posting Advertisement in Mass). SMS spam merupakan pesan teks yang tidak diinginkan atau diminta, seperti iklan, jasa, dan potensi penipuan yang dapat merugikan pengguna. Indonesia tercatat sebagai negara di Asia dengan jumlah pesan spam tertinggi pada tahun 2020. Untuk meminimalisir korban pesan sp

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Nosiel, Nosiel, Sigit Andriyanto, and Muhammad Said Hasibuan. "Application of Nave Bayes Algorithm for SMS Spam Classification Using Orange." International Journal of Advanced Science and Computer Applications 1, no. 1 (2021): 16–24. http://dx.doi.org/10.47679/ijasca.v1i1.5.

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Mobile phones have become a necessity for everyone. SMS is a communication service that is used to send and receive short messages in the form of text on mobile phones. Among all the advantages of SMS, there is a very annoying activity called spam (unsolicited commercial advertisements). Spam is the continuous use of electronic devices to send messages. called spammers. Spam messages are sent by advertisers with the lowest operating costs. Therefore, there are a lot of spammers and the number of messages requested is huge. Therefore, many aspects are harmed and disturbed. When SMS enters the u

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Ma, Jialin, Yongjun Zhang, Lin Zhang, Kun Yu, and Jinlin Liu. "Bi-Term Topic Model for SMS Classification." International Journal of Business Data Communications and Networking 13, no. 2 (2017): 28–40. http://dx.doi.org/10.4018/ijbdcn.2017070103.

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With the overflowing of Short Message Service (SMS) spam nowadays, many traditional text classification algorithms are used for SMS spam filtering. Nevertheless, because the content of SMS spam messages are miscellaneous and distinct from general text files, such as more shorter, usually including mass of abbreviations, symbols, variant words and distort or deform sentences, the traditional classifiers aren't fit for the task of SMS spam filtering. In this paper, the authors propose a Short Message Biterm Topic Model (SM-BTM) which can be used to automatically learn latent semantic features fr

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Kandula, Srikanth. "Spam or Ham? A Hybrid Deep Learning Approach for SMS Spam Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 104–8. https://doi.org/10.22214/ijraset.2025.67164.

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With the advent of digital communications, SMS spam has also become a widespread issue, which is inconvenient and even threatening to users. In this project, we advocate a hybrid spam detection model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and TF-IDF, and efficiently leverages deep learning and text-processing methods to identify spam messages. We train our model using the publicly available UCI SMS spam dataset. The interface gives an easy and convenient means of classifying SMS messages. Upon providing a message for classification, the model proc

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Nagwani, Naresh Kumar, and Aakanksha Sharaff. "SMS spam filtering and thread identification using bi-level text classification and clustering techniques." Journal of Information Science 43, no. 1 (2016): 75–87. http://dx.doi.org/10.1177/0165551515616310.

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SMS spam detection is an important task where spam SMS messages are identified and filtered. As greater numbers of SMS messages are communicated every day, it is very difficult for a user to remember and correlate the newer SMS messages received in context to previously received SMS. SMS threads provide a solution to this problem. In this work the problem of SMS spam detection and thread identification is discussed and a state of the art clustering-based algorithm is presented. The work is planned in two stages. In the first stage the binary classification technique is applied to categorize SM

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Herwanto, Herwanto, Nuke L. Chusna, and Muhammad Syamsul Arif. "Klasifikasi SMS Spam Berbahasa Indonesia Menggunakan Algoritma Multinomial Naïve Bayes." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1316. http://dx.doi.org/10.30865/mib.v5i4.3119.

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Based on a report submitted by Truecaller Insights Report 2020, Indonesia placed sixth position with the most spam messages, one of the spam applications is SMS. Spam SMS contains unwanted or unsolicited messages, including advertisements, scams and so on. The existence of this spam message causes inconvenience from the user's side when receiving spam SMS, and some even become victims of crime after responding to the SMS. To minimize inconvenience and crime caused by spam messages, the purpose of this study is to filter SMS spam or SMS filtering by classifying SMS spam using the Multinomial Na

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Shaniya, Chauhan. "SMS Spam Classifier Using Machine Learning." International Journal of Innovative Science and Research Technology 8, no. 6 (2023): 29–30. https://doi.org/10.5281/zenodo.8021622.

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The purpose of this research paper is to examine how machine learning techniques are used to identify whether a SMS is spam or not. The growth of mobile users has led to a dramatic increase in SMS and messages. Despite the fact that our idea of information channels are currently seen as spotless and reliable in many parts of the world on going data clearly demonstrates that the amount of cell phones Spam is dramatically increasing overtime. It is a growing catastrophe, especially in the middle East and Asia. Separating SMS spam is a similarly lead task to solve this problem. It games several c

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J, Mrs Mounika. "SMS SPAM DETECTION WITH MULTINOMIAL NAIVE BAYES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29878.

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SMS (short messaging service) usage has increased dramatically as a result of the growth in mobile users, enabling text messaging between smartphone and landline users. But there has also been a noticeable increase in unsolicited communications, or spam, coinciding with this growth in SMS usage. Through marketing campaigns and attempts to gain private information, such as credit card numbers, these spam messages seek to further business or financial objectives. The duty of removing spam mails has therefore grown in significance. In response, a number of deep learning and machine learning metho

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Azzahra, Fathimah Noer, Tatang Rohana, Rahmat Rahmat, and Ayu Ratna Juwita. "Penerapan Metode Naive Bayes Dalam Klasifikasi Spam SMS Menggunakan Fitur Teks Untuk Mengatasi Ancaman Pada Pengguna." Journal of Information System Research (JOSH) 5, no. 3 (2024): 873–80. https://doi.org/10.47065/josh.v5i3.5070.

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One of the negative impacts of current digital advances is the increasing number of SMS spam. Spam SMS poses a security risk to users because they can contain malicious links or requests for personal information that are used for malware, smishing, or fraud attacks. However, with the various protection measures available, not all spam SMS can be classified and prevented effectively. However, this problem can be minimized by creating an anti-spam SMS model which aims to classify SMS types. So this research aims to classify types of SMS that contain spam and spam by applying the Naïve Bayes algo

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Manikkannan, Prof. "SMS Spam Detection using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27463.

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SMS spam detection using Naive Bayes algorithm is a widely used technique in the field of text classification. The main aim of this approach is to classify the incoming messages into spam or ham categories. The Naive Bayes algorithm works by calculating the probability of a message belonging to a particular class, based on the occurrence of different words in the message. In this paper, we present an efficient and accurate approach for SMS spam detection using the Naive Bayes algorithm. The proposed approach utilizes a pre- processing step for feature extraction, which includes tokenization, s

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Kale, Atharva. "Spam SMS Detection Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50094.

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Abstract - In today's digital communication era, unsolicited and malicious text messages, commonly known as spam, pose a significant threat to user privacy and mobile security. This project aims to develop an intelligent and automated system for SMS spam detection using machine learning techniques, with a focus on the Support Vector Machine (SVM) algorithm. The objective is to classify incoming messages as either "spam" or "ham" (non-spam) with high accuracy and efficiency. The system is trained on a labeled SMS dataset containing a mixture of spam and ham messages. Text preprocessing techniqu

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Amin, Muhammad Basil Musyaffa, Gibran Hakim, Muhammad Taufik Maulana, et al. "Deteksi Spam Berbahasa Indonesia Berbasis Teks Menggunakan Model Bert." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 6 (2024): 1291–302. https://doi.org/10.25126/jtiik.1168121.

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Spam pada SMS dan Email menyebabkan pengalaman kurang menyenangkan bagi pengguna dalam pemanfaatan teknologi. Spam secara umum merupakan sebuah tindakan mengirim pesan yang tidak diinginkan atau tidak diminta kepada sejumlah besar orang. Spam kini dapat ditemui dalam berbagai bentuk, seperti web maupun multimedia. Penelitian ini bertujuan untuk mengevaluasi model berbasis BERT, khususnya IndoBERT dan MultilingualBERT, dalam mendeteksi dan mengklasifikasi spam berbahasa Indonesia pada pesan SMS dan Email. Model yang dipilih kemudian dilatih untuk mengidentifikasi perbedaan antara pesan spam dan

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Amin, Muhammad Basil Musyaffa, Gibran Hakim, Muhammad Taufik Maulana, et al. "Deteksi Spam Berbahasa Indonesia Berbasis Teks Menggunakan Model Bert." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 6 (2024): 1291–302. https://doi.org/10.25126/jtiik.2024118121.

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Spam pada SMS dan Email menyebabkan pengalaman kurang menyenangkan bagi pengguna dalam pemanfaatan teknologi. Spam secara umum merupakan sebuah tindakan mengirim pesan yang tidak diinginkan atau tidak diminta kepada sejumlah besar orang. Spam kini dapat ditemui dalam berbagai bentuk, seperti web maupun multimedia. Penelitian ini bertujuan untuk mengevaluasi model berbasis BERT, khususnya IndoBERT dan MultilingualBERT, dalam mendeteksi dan mengklasifikasi spam berbahasa Indonesia pada pesan SMS dan Email. Model yang dipilih kemudian dilatih untuk mengidentifikasi perbedaan antara pesan spam dan

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Ajat, Muhamad Hajat Syafii. "KLASIFIKASI SMS SPAM DENGAN KOMPARASI METODE SVM DAN NAÏVE BAYES." METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi 9, no. 1 (2023): 31–34. http://dx.doi.org/10.46880/mtk.v9i1.1694.

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Perkembangan teknologi memaksa sebagian besar penduduk diseluruh dunia termasuk di Indonesia untuk dapat memanfaatkan kemajuan tersebut. Salah satu teknologi yang dimaksud adalah internet dan Gadget. Perkembangan smartphone yang pesat tidak merubah fungsi dari salah satu layanan provider yaitu layanan pesan singkat atau short message service (SMS). SMS saat ini masih dipergunakan untuk mengirimkan pesan kepada pengguna yang sudah saling kenal maupun kepada orang yang belum dikenal, dengan banyak tujuan diantaranya untuk menawarkan produk atau jasa. Hal tersebut merupakan permasalahan untuk dap

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Chrysanti, Rachma, Sony Hartono Wijaya, and Toto Haryanto. "The Development of Classification Algorithm Models on Spam SMS Using Feature Selection and SMOTE." ILKOM Jurnal Ilmiah 16, no. 3 (2024): 356–70. https://doi.org/10.33096/ilkom.v16i3.2220.356-370.

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Short Message Service (SMS) is a widely used communication media. Unfortunately, the increasing usage of SMS has resulted in the emergence of SMS spam, which often disturbs the comfort of cellphone users. Developing a classification model as a solution for filtering SMS spam is very important to minimize disruption and loss to cellphone users due to SMS spam. To address this issue, utilize the Naïve Bayes algorithm and Support Vector Machine (SVM) along with Chi-square and Information Gain. This study focuses on the classification and analysis of SMS spam on a cellular operator service in a te

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Smt., A. Sireesha Sana Bhanu Karthik K. Srena Surapaneni Nanda Gopal Sadi Karthik Reddy. "SMS Spam Detection Using Machine Learning." Scandinavian Journal of Information Systems 35, no. 1 (2023): 749–54. https://doi.org/10.5281/zenodo.7807440.

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These days usage of mobiles is increasing rapidly. SMS (Short message service) is available on any type of mobile phone to contact and share information. We are using this SMS for OTPs, conversations, etc…. So, the SMS importance and usage increased rapidly with this Spam messages also increased day by day regarding business, credit cards, loans, lottery tickets, etc… In this paper, we collect the dataset from Kaggle.com and use different types of ML techniques for messages detection. We achieved the highest accuracy with the Random Forest classifier of 97.90% for SMS SPAM Detect

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GIANNELLA, CHRIS R., RANSOM WINDER, and BRANDON WILSON. "(Un/Semi-)supervised SMS text message SPAM detection." Natural Language Engineering 21, no. 4 (2014): 553–67. http://dx.doi.org/10.1017/s1351324914000102.

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AbstractWe address the problem of unsupervised and semi-supervised SMS (Short Message Service) text message SPAM detection. We develop a content-based Bayesian classification approach which is a modest extension of the technique discussed by Resnik and Hardisty in 2010. The approach assumes that the bodies of the SMS messages arise from a probabilistic generative model and estimates the model parameters by Gibbs sampling using an unlabeled, or partially labeled, SMS training message corpus. The approach classifies new SMS messages as SPAM or HAM (non-SPAM) by zero-thresholding their logit esti

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Adel Al-Zebari. "Deep Learning Hybrid Approach for Accurate SMS Spam Identification." Journal of Information Systems Engineering and Management 10, no. 10s (2025): 619–35. https://doi.org/10.52783/jisem.v10i10s.1426.

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Short messaging service (SMS) is a popular application for mobile devices. People often use SMS when they are not suitable for voice calls. Nowadays, SMS is used for commercial purposes. These SMS can sometimes be useful. But sometimes, unwanted SMS, which is called spam SMS, can disturb mobile phone users. Thus, spam SMS detection becomes an important application for mobile phone service providers. Up to now, machine-learning approaches have been used for spam SMS detection. These approaches used various supervised learning methods for detection purposes. In this paper, three deep-learning ap

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Oyeyemi, Dare Azeez, and Adebola K. Ojo. "SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing." Journal of Advances in Mathematics and Computer Science 38, no. 10 (2023): 144–56. http://dx.doi.org/10.9734/jamcs/2023/v38i101832.

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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. Despit

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Mounika, Surabatthini. "Enhanced Security and Reliability in Messaging Systems through Real-Time SMS Spam Filtering with Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48995.

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Abstract— SMS spam has emerged as a major issue for cellular users, which leads to annoyance and inconvenience. Machine learning has been effective in filtering out spam SMS. Yet, applying these techniques in actual real-time situations poses special challenges. A newly published study seeks to tackle these challenges by building an efficient real-time. SMS spam filtering system based on machine learning. The main contribution of this work is to improve the performance of the system in real-time classification by focusing on data preparation, feature engineering, algorithm selection, and model

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Jalal Mussa, Diyari, and Noor Ghazi M. Jameel. "Relevant SMS Spam Feature Selection Using Wrapper Approach and XGBoost Algorithm." Kurdistan Journal of Applied Research 4, no. 2 (2019): 110–20. http://dx.doi.org/10.24017/science.2019.2.11.

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In recent years with the widely usage of mobile devices, the problem of SMS Spam increased dramatically. Receiving those undesired messages continuously can cause frustration to users. And sometimes it can be harmful, by sending SMS messages containing fake web pages in order to steal users’ confidential information. Besides spasm number of hazardous actions, there is a limited number of spam filtering software. According to this paper, XGBoost algorithm used for handling SMS spam detection problem. Number of structural features was collected from previous studies. 15 structural features were

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Raharja, Pradana Ananda, Muhammad Fajar Sidiq, and Diandra Chika Fransisca. "Comparative Analysis of Multinomial Naïve Bayes and Logistic Regression Models for Prediction of SMS Spam." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 3 (2022): 1290. http://dx.doi.org/10.30865/mib.v6i3.4019.

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This research was conducted based on a report from the United States Federal Trade Commission regarding fraud through electronic text messages via SMS that fraudsters use to manipulate potential victims. Usually, scammers spread SMS spam as an intermediary for the crime. The development of a supervised learning algorithm is applied to predict SMS spam into three categories, such as SMS spam, SMS fraud, and promotional SMS. The prediction system is dividing into several stages in the development process, including data labelling, data preprocessing, modelling, and model validation. The known ac

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Utami, Lila Dini, Lestari Yusuf, and Dini Nurlaela. "Komparasi Algoritma Naïve Bayes dan Support Vectors Machine pada Analisis Sentimen SMS HAM dan SPAM." Infotek : Jurnal Informatika dan Teknologi 4, no. 2 (2021): 249–58. http://dx.doi.org/10.29408/jit.v4i2.3665.

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SMS is a form of communication in the form of messages sent using mobile phones between the designated numbers. SMS is now rarely used because many of the features that have changed are used by chat applications. However, the SMS feature was not removed for one thing, official messages from various applications for leveraging or other official information still use SMS as a sign that the phone number used is there. However, since 2011 there have been so many misuses of this function, so it is suspected that many frauds use SMS as a tool to influence victims. This sms category goes to SMS spam.

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Setiyono, Agus, and Hilman F. Pardede. "KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE." Jurnal Pilar Nusa Mandiri 15, no. 2 (2019): 275–80. http://dx.doi.org/10.33480/pilar.v15i2.693.

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It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial

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Alqahtani, Sahar, and Daniyal Alghazzawi. "A survey of Emerging Techniques in Detecting SMS Spam." Transactions on Machine Learning and Artificial Intelligence 7, no. 5 (2019): 23–35. http://dx.doi.org/10.14738/tmlai.75.7116.

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In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various

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Kapila, Pooja. "SMS Spam Detection Using Machine Learning and Deep Learning Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47677.

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Abstract – The rapid development of mobile technology has brought with it the challenge of dealing with SMS spam, which has become a major concern for users’ privacy and telecommunication systems within networks. Apart from causing inconveniences, SMS spam can also lead to phishing, financial fraud, and even the spread of malware. This paper focuses on recent studies on SMS spam detection that utilized Machine Learning (ML) and Deep Learning (DL) technologies. Focus is given to model selection, dataset compilation, preprocessing steps, evaluation benchmarks, and explainability for performance

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Xia, Tian, and Xuemin Chen. "A Discrete Hidden Markov Model for SMS Spam Detection." Applied Sciences 10, no. 14 (2020): 5011. http://dx.doi.org/10.3390/app10145011.

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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each wor

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Gupta, Suparna Das, Soumyabrata Saha, and Suman Kumar Das. "SMS Spam Detection Using Machine Learning." Journal of Physics: Conference Series 1797, no. 1 (2021): 012017. http://dx.doi.org/10.1088/1742-6596/1797/1/012017.

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Suleiman, Dima, and Ghazi Al-Naymat. "SMS Spam Detection using H2O Framework." Procedia Computer Science 113 (2017): 154–61. http://dx.doi.org/10.1016/j.procs.2017.08.335.

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Xu, Qian, Evan Wei Xiang, Qiang Yang, Jiachun Du, and Jieping Zhong. "SMS Spam Detection Using Noncontent Features." IEEE Intelligent Systems 27, no. 6 (2012): 44–51. http://dx.doi.org/10.1109/mis.2012.3.

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Roy, Pradeep Kumar, Jyoti Prakash Singh, and Snehasish Banerjee. "Deep learning to filter SMS Spam." Future Generation Computer Systems 102 (January 2020): 524–33. http://dx.doi.org/10.1016/j.future.2019.09.001.

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Delany, Sarah Jane, Mark Buckley, and Derek Greene. "SMS spam filtering: Methods and data." Expert Systems with Applications 39, no. 10 (2012): 9899–908. http://dx.doi.org/10.1016/j.eswa.2012.02.053.

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Airlangga, Gregorius. "Optimizing SMS Spam Detection Using Machine Learning: A Comparative Analysis of Ensemble and Traditional Classifiers." Journal of Computer Networks, Architecture and High Performance Computing 6, no. 4 (2024): 1942–51. http://dx.doi.org/10.47709/cnahpc.v6i4.4822.

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With the rapid rise of mobile communication, Short Message Service (SMS) has become an essential platform for transmitting information. However, the growing volume of unsolicited and harmful spam messages presents significant challenges for both users and mobile network operators. This study explores the effectiveness of various machine learning models, including Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, and an Ensemble Voting Classifier, in detecting SMS spam. A dataset containing 5,572 SMS messages, labeled as either spam or ham (legitimat

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Rashmi Pandey, Pushpendra Prajapati, Vibhanshu Kumar Singh, Mayank Tyagi, and Chetan Anand Amb. "SMS Spam Filteration Using Text Features and Supervised Machine Learning Algorithms." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 641–51. http://dx.doi.org/10.32628/cseit2410452.

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Over time, technological advancements have had an immense effect on every aspect of life, including travel, office work, music, healthcare, and communication. In the past, people communicated using telephone lines. With far more functionality than telephone cable technology, wireless technology already prevails. SMS is mostly used by spammers and advertising firms to communicate with the general public and distribute company pamphlets. This explains why over 60% of spam SMS are sent and received every day. Although these spam communications irritate users and occasionally con unsuspecting user

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Lokam., Devi Naga Srinu, Dhanush Kumar Meesala., and Mani Gopal Mulaparthi. "Message Spam Identification by Naive Bayes Classifier Algorithm using Machine Learning." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 3 (2024): 5. https://doi.org/10.5281/zenodo.10795930.

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With the spread of modern life, messaging has become one of the most important forms of communication. SMS (Short Message Service) is a text messaging service available on all smart phones and  mobiles. Facebook, WhatsApp etc. Unlike other chat- based communication applications, SMS does not require  any internet connection. SMS traffic has increased significantly and spam has also been increased rapidly. Hackers and spammers are trying to scam over devices through SMSs. As a result, SMS support for mobile devices becomes difficult. Spammers may ask for business expansion, lottery in

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Setifani, Niken Ayu, Devinta Nurul Fitriana, and Ahmad Yusuf. "PERBANDINGAN ALGORITMA NAÏVE BAYES, SVM, DAN DECISION TREE UNTUK KLASIFIKASI SMS SPAM." JUSIM (Jurnal Sistem Informasi Musirawas) 5, no. 02 (2020): 153–60. http://dx.doi.org/10.32767/jusim.v5i02.956.

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Abstrak Perkembangan teknologi semakin memudahkan kegiatan manusia dan hampir semua kalangan memiliki ponsel. Sehingga ponsel menjadi alat yang penting dalam berkomunikasi bagi kebanyakan orang terutama SMS. Banyaknya pesan yang masuk bisa tidak memungkinkan untuk mengklasifikasikan SMS spam secara manual. Untuk itu dilakukan pengklasifikasian SMS spam menggunakan teknik klasifikasi dalam data mining. Banyaknya algoritma yang tersedia memungkinkan kita untuk menggunakan salah satunya sebagai algoritma terbaik untuk klasifikasi SMS spam. Untuk itu dilakukan pengujian beberapa algoritma kl

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Mr. Ravi H Gedam and Dr. Sumit Kumar Banchhor. "The Impact of Integrating Machine Learning and Block Chain for SMS Spam Detection." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 592–600. https://doi.org/10.47392/irjaeh.2025.0083.

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The widespread use of mobile communication has led to a significant rise in SMS-based spam, posing challenges for users and service providers. This paper explores the integration of machine learning (ML) and block chain technology to enhance SMS spam detection. We assess the effectiveness of various ML algorithms in identifying spam messages and examine the potential of blockchain to provide a secure, decentralized platform for data sharing and model updates. Our findings indicate that combining ML and blockchain can significantly improve the accuracy and reliability of SMS spam detection, off

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Wu, Hongli, and Yong Hui Jiang. "SMS Spam Filtering Based on “Cloud Security”." Applied Mechanics and Materials 263-266 (December 2012): 2015–19. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2015.

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“Cloud Computing” technology has very big advantage in the computing power, scalability, reliability and cost etc. “Cloud Security "and " Cloud Storage " is one of the two main research fields. This paper puts forward “filter cloud” strategies of filter spam messages based on "Cloud Security" in order to achieve the purpose of filtering spam messages by addressing its root causes. It is a new attempt that applying “Cloud Computing” in spam messages filter and more mobile business would move to "cloud computing" platform in the future.

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Apriansyah, Ferryma Arba, Arief Hermawan, and Donny Avianto. "Optimization of K Value in KNN Algorithm for Spam and HAM Classification in SMS Texts." International Journal Software Engineering and Computer Science (IJSECS) 4, no. 2 (2024): 767–79. http://dx.doi.org/10.35870/ijsecs.v4i2.2681.

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Spam refers to the unsolicited and repetitive sending of messages to others via electronic devices without their consent. This activity, commonly known as spamming, is typically carried out by individuals referred to as spammers. SMS spam, which often originates from unknown sources, frequently contains advertisements, phishing attempts, scams, and even malware. Such spam messages can be pervasive, affecting almost all mobile phone numbers, thereby causing significant disruptions to communication by delivering irrelevant content. The persistent nature of spam messages underscores the need for

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