Naive Bayes Classifier-Based Smishing Detection Framework to Reduce Cyber Attack

With the advancement of IT innovation, mobile computing expertise has lately become more widely used by humans. Through the use of sophisticated devices like tablet PCs, smartphones, and other mobile computing devices, a comfortable atmosphere has been created. There are lots of potential threats in the world of mobile technology. Protective components are thus required to guard against safety concerns, especially the Short Text System. The harm caused by spoofing has kept rising as mobile computing environments have become more common. In this research, we examined the privacy concerns around smishing in contexts that are used in mobile computing. Additionally, we provide a strengthened security framework for identifying smishing attacks. The proposed approach enhances the Naive Bayes classification method to enhance the identification of Smishing attacks in connected phones. This framework distinguishes between legitimate text messages and fraudulent ones. The computational intelligence technique is primarily utilized to select applying information. It is therefore feasible to look into a phone message and successfully identify SMS phishing. Furthermore, we evaluate and analyze our proposed approach to show the effectiveness of the approach.