International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Optimizing SMS Spam Detection: A Hybrid Approach Using Support Vector Classifiers and Catboost |
|---|---|
| ABSTRACT | The project "SMS Spam Detection using Machine Learning" addresses the challenge of identifying spam messages in SMS communication by leveraging advanced machine learning techniques. Implemented using Python for backend processing and integrated with a user-friendly frontend crafted in HTML, CSS, and JavaScript, this web application utilizes the Flask framework to ensure a seamless and responsive user experience. The core of this project involves the development and deployment of two distinct machine learning models to classify SMS messages as either spam or ham (non-spam). The first model employs a Support Vector Classifier (SVC), which achieved an impressive training accuracy of 99.2% and a test accuracy of 98.30%. This high level of precision underscores the model's robustness and reliability in distinguishing between spam and legitimate messages. In parallel, a CatBoost classifier, which is based on gradient boosting, was also developed and evaluated. This model demonstrated a training accuracy of 97.76% and a test accuracy of 97.19%, showcasing its effectiveness and efficiency in handling the classification task with a marginally lower, yet still commendable, performance compared to the SVC. The dataset used for training and evaluation comprises 67,010 instances with two attributes, one of which is the target attribute for classification. The substantial size of the dataset ensures the models are well-trained and capable of generalizing effectively to new, unseen data. Overall, the project exemplifies the application of cutting-edge machine learning methodologies in solving real-world problems, providing a robust tool for SMS spam detection. The integration with a modern web framework ensures accessibility and ease of use, making it a valuable resource for individuals and organizations aiming to filter and manage SMS communications effectively. |
| AUTHOR | SAMAVEDAM V S S SRINIVASA KUMAR, M. LATHA PG Student, Dept. of CSE, Siddartha Educational Academy Group of Institutions, Tirupati, India Assistant Professor, Dept. of CSE(AI&ML), Siddartha Educational Academy Group of Institutions, Tirupati, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406022 |
| pdf/22_Optimizing SMS Spam Detection A Hybrid Approach Using Support Vector Classifiers and Catboost.pdf | |
| KEYWORDS | |
| References | [1] Delvia, D., Arifin, A. S., & Bijaksana, M. A. (2016). Enhancing Spam Detection on Mobile Phone Short Message Service (SMS) Performance using FP-Growth and Naive Bayes Classifier. International Journal of Computer Applications. [2] Gupta, S. D., Saha, S., & Das, S. K. (2020). SMS Spam Detection Using Machine Learning. IEEE International Conference on Computing and Communication Systems. [3] Julis, M. R., & Alagesan, S. (2020). Spam Detection In SMS Using Machine Learning Through Text Mining. International Journal of Advanced Trends in Computer Science and Engineering. [4] Masurkar, S., & Singh, A. (2022). Machine Learning Based Spam Detection System. Journal of Network Security. [5] Navaney, P., & Dubey, G. (2022). SMS Spam Filtering using Supervised Machine Learning Algorithms. International Journal of Computer Science and Network Security. [6] Ramhari, S. (2023). Classification Methods for Spam Detection in Online Social Networks. International Journal of Information Systems. [7] Shirani-Mehr, H. (2017). SMS Spam Detection using Machine Learning Approach. Journal of Intelligent Systems. [8] Sisodiya, A. G., et al. (2022). Implementation of Spam Classifier using Naïve Bayes Algorithm. International Journal of Information Systems Engineering and Management. [9] Smith, J. R., et al. (2026). Combating Mobile Messaging Spam: Modern Protection Strategies for SMS and Chat Platforms. Journal of Information Systems Engineering and Management. [10] Varma, A., et al. (2025). Comparative Analysis of SMS Spam Detection employing Machine Learning Methods. IIETA Journal of Information Systems. |
