Enhanced Spam Detection in Short Message Service using Hybrid Techniques
- Title
- Enhanced Spam Detection in Short Message Service using Hybrid Techniques
- Creator
- Raj, E. Babu; Barona, R.; Mary, N. Ansgar; George, Shiju
- Description
- Receiving unwanted text messages, or SMS spam, costs consumers time and money and poses a security concern. To address this issue, we can deploy a system that recognizes and automatically filters out undesirable messages. This method, a testament to the advancement in technology, employs machine learning algorithms that gain knowledge from a pool of communications classified as spam or not. Managing various message contents and languages is one of the system's unique challenges. Notwithstanding these challenges, the approach may be effective in reducing unsolicited communications, improving the security of people's mobile devices and saving them time and money. To address this issue, a variety of machine learning approaches have been employed, ranging from more modern deep learning methods like Convolutional Neural Networks (CNNs) to more traditional ones like Naive Bayes. It is common practice to assess the effectiveness of SMS spam classifiers using measures like as F1-score, precision, and recall. All things considered, SMS spam classification is crucial for protecting the security and privacy of mobile phone users and has useful applications in everyday situations. Grenze Scientific Society, 2025.
- Source
- 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;1;pp.4203-4209
- Date
- 01-01-2025
- Publisher
- Grenze Scientific Society
- Subject
- Convolutional Neural Network (CNN); Machine Learning; Nae Bayes; SMS Spam
- Coverage
- Raj E.B., Christ University, Bengaluru, Bengaluru, India; Barona R., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; Mary N.A., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; George S., Christ University, Bengaluru, Bengaluru, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Raj, E. Babu; Barona, R.; Mary, N. Ansgar; George, Shiju, “Enhanced Spam Detection in Short Message Service using Hybrid Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26241.
