Classify SMS in Mobile System Using IBK and K-Star
Neetu Gupta, Sonal Arora · Journal of Emerging Technologies and Innovative Research · 2020
The intense growth of smart mobile phones and users has contributed to the expansion of online or offline Instant Messaging and SMS usage as an alternate way of Transaction and communication. Along with the faith they instinctively have in their devices, it makes this kind of message a congenial environment for spammers. In fact, reports distinctly show that the volume of spam over Instant Messaging and SMS is rapidly increasing year by year. This represents a challenging problem for classical filtering methods these days. Smishing this term represents phishing in SMS/Messages called SMS-phishing is a cyber-security attack, which utilizes Short Message Service (SMS) to steal personal data/credentials of mobile users. The faith level of mobile users on their smartphones has attracted attackers to perform various mobile security attacks like SMS-Phishing. In this paper, we implement the SMS-Case-based data mining classification approach to classify them subpart of the SMS category by detecting legitimate, Illegitimate/Smishing messages and these classified messages will further categorize in three parts Primary, Other, Fake. In this research paper IBL and K-Start are used to classify the SMS and moreover we will analyze the classified data model in detail using the Weka tool. During the lockdown period due to Pandemic affected by COVID19 SMS- Phishing becomes more active and the attackers send fraud messages to the mobile users intensively