A Machine Learning Approach for Efficient Spam Detection in Short Messaging System (SMS)

Short Message Service (SMS) is a generally used communication method due to its convenience and affordability. SMS spam message is an unauthorized text message that contains a variety of content types such as advertisements, fraudulent texts, and promotions. These messages can pose a serious threat to mobile phone users as they may contain security threats, malicious activities, and other concerning issues. These can lead to identity theft, financial loss, and other types of fraud. To deal with the problem of spamming, various machine-learning models are applied to develop an optimized model that effectively, reliably, and precisely identifies and filter out spam or junk message from a genuine SMS text. The dataset used is a combination of self-acquired data and internet collected dataset with 60–40 ham to spam partitions. With regards to the accuracy of the model, the Bernoulli Naive Bayes achieved the highest performance with 96.63% accuracy upon optimization.