Short Message Service (SMS) Mobile Spam Detection using Naïve Bayes
Samadhan M. Nagare, Pratibha P. Dapke, Syed Ahteshamuddin Quadri, Sagar Balasaheb Bandal, Manasi Ram Baheti · 2024
The number of mobile users worldwide is growing, which is contributing to a sharp rise in SMS spam. The fact that bulk pre-pay SMS packages are now easily accessible and that SMS is regarded as a reliable and private service has led to recent findings clearly indicating that an equivalent SMS spam may be a real and active disadvantage. This paper describes the how naïve bayes algorithm is applied for detecting and categorization spam SMS although naïve by is used commonly used algorithm but it has the effectiveness for the base data The network is experiencing an exponential increase in information traffic, which makes the connected devices extremely vulnerable. Network security is therefore extremely important in protecting our system from this kind of vulnerability, as there is a greater need to do so. This paper uses The UCI Machine Learning repository contains an SMS spam dataset. Before being exposed to a range of machine learning techniques, including Naïve Bayes (NB), the dataset is pre-processed. The performance of the algorithms is then computed.