Relevant bibliographies by topics / Spam SMS detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Spam SMS detection'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Spam SMS detection"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Spam SMS detection"

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Jaroš, Ján. "Detekce nevyžádaných zpráv v mobilní komunikaci a na sociálních sítích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236082.

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This thesis deals with spam in mobile and social networks. It focuses on spam in SMS messages and web service Twitter. Theoretical part provides brief overview of those two media, informations about what spam is, how to defend against it and where does it comes from. There is also a list of methods for spam detection, many of them have their roots in filtration of email communication. The rest of thesis is about design, implementation of application  for spam detection in SMS and Twitter messages and evaluation of its performance.
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Books on the topic "Spam SMS detection"

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El gobierno como problema. Teseo, 2019. http://dx.doi.org/10.55778/ts877837254.

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<p>Esta obra se refiere de distintas maneras a los estudios en gubernamentalidad. Así, contiene reflexiones que expanden y profundizan determinados desarrollos de Foucault al respecto, ya sea trabajando en una continuidad a partir de sus tesis acerca del neoliberalismo norteamericano e indagando en la relación del derecho con el gobierno del mercado, ya sea pensando la dimensión estratégica del análisis de las racionalidades gubernamentales. También resultan materia de estudio aquí las obras de Carol <span style="white-space: nowrap;">Bacchi</span> y Nikolas Rose: se rescata
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Book chapters on the topic "Spam SMS detection"

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Srinivasa Rao, D., and E. Ajith Jubilson. "SMS Spam Detection Using Federated Learning." In Proceedings of International Conference on Computational Intelligence and Data Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0609-3_39.

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Debnath, Kingshuk, and Nirmalya Kar. "SMS Spam Detection Using Deep Learning Approach." In Human-Centric Smart Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5403-0_29.

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Basumatary, Baknai, Tenzin Khetsuen Norbu, Sujatha Arun Kokatnoor, and Sandeep Kumar. "Machine Learning Models for SMS Spam Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2647-2_29.

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El Hlouli, Fatima Zohra, Jamal Riffi, Mohamed Adnane Mahraz, Ali El Yahyaouy, and Hamid Tairi. "Detection of SMS Spam Using Machine-Learning Algorithms." In Embedded Systems and Artificial Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0947-6_41.

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Birthare, Megha, Neelesh Jain, and Alpana Meena. "Machine Learning Techniques on Mobile SMS Spam Detection." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-716-8_7.

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Sharaff, Aakanksha. "Spam Detection in SMS Based on Feature Selection Techniques." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1498-8_49.

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Terli, Niharika, Pavan Chintakayala, Venu Madhavi Angaluri, and Suhasini Sodagudi. "Detection of Spam in SMS Using Machine Learning Algorithms." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0838-7_37.

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Althubiti, Sara A., E. Laxmi Lydia, K. Vijaya Kumar, and Kommu Gangadhara Rao. "Hilbert–Huang Transform Framework-Based Email and SMS Spam Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7880-5_17.

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Ramanujam, E., K. Shankar, and Arpit Sharma. "A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6690-5_40.

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Anh, Hoang Quang, Pham Tuan Anh, Pham Son Nguyen, and Phan Duy Hung. "Federated Learning for Vietnamese SMS Spam Detection Using Pre-trained PhoBERT." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77731-8_24.

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Conference papers on the topic "Spam SMS detection"

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Alzubaidi, Ali, and Sakher Ghanem. "Arabic SMS Spam Detection." In 2025 International Conference on Innovation in Artificial Intelligence and Internet of Things (AIIT). IEEE, 2025. https://doi.org/10.1109/aiit63112.2025.11082859.

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Rajput, Satpalsing D., Pratiksha Chopade, Atharva Chivate, Shreeshail Chitpur, and Isha Dashetwar. "Spam SMS Detection Using Natural Language Processing." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774959.

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Rajalakshmi, M., R. Rengaraj, M. Karthikeyan, and Aruna S. "NLP-Based SMS Spam Detection Using Ensemble Learning." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10779991.

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Nayak, Amlan, Rina Kumari, Debapam Pal, Sudatta Jana, Aniket Bhardwaj, and Pratim Mangaldas Dasude. "Multilingual SMS Spam Detection using BERT and LSTM." In 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET). IEEE, 2024. http://dx.doi.org/10.1109/icicet59348.2024.10616322.

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Subhashini, G., Mahalakshmi G., H. Mohamed Ashik, and B. Nithin Duresh. "Advanced SMS Spam Detection Using Integrated Feature Extraction." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10867034.

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Arora, Yojna, Neha Gupta, Yogesh Singh Rathore, et al. "SMS Spam Detection using Advance Naive-Bayes Approach." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10940832.

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Sultana, Hafsa, Jamal Uddin Tanvin, Fairooz Tasnia, and Nusrat Sharmin. "Bilingual SMS Spam Detection Using Deep Ensemble Learning." In 2024 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2024. https://doi.org/10.1109/wiecon-ece64149.2024.10915168.

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Nagare, Samadhan M., Pratibha P. Dapke, Syed Ahteshamuddin Quadri, Sagar B. Bandal, Ramnath M. Gaikwad, and Manasi R. Baheti. "Pre-Processing Techniques for Mobile SMS Spam Detection." In 2024 IEEE Pune Section International Conference (PuneCon). IEEE, 2024. https://doi.org/10.1109/punecon63413.2024.10895745.

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Shdefat, Ahmed Younes, Nouran M. Sedky, Zeina H. El Bialy, Shahd Ahmed, Hanaa Fathi, and Diaa Salama AbdElminaam. "Machine Learning-Based Solution for SMS Spam Detection Problem." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652878.

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Vats, Satvik, Suryakant Shastri, and Shiva Mehta. "Federated Learning for SMS Spam Detection: A Privacy-Focused Approach." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724879.

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