A Naive Bayes classifier with NoSlang dictionary normalization detects smishing messages at 96.2 percent accuracy on a hand-built subset of the SMS Spam Collection.
Designing a mobile game to thwarts malicious IT threats: A phishing threat avoidance perspective
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Phishing is an online identity theft, which aims to steal sensitive information such as username, password and online banking details from victims. To prevent this, phishing education needs to be considered. Game based education is becoming more and more popular. This paper introduces a mobile game prototype for the android platform based on a story, which simplifies and exaggerates real life. The elements of a game design framework for avoiding phishing attacks were used to address the game design issues and game design principles were used as a set of guidelines for structuring and presenting information. The overall mobile game design was aimed to enhance the user's avoidance behaviour through motivation to protect themselves against phishing threats. The prototype mobile game design was presented on MIT App Inventor Emulator.
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Detection and Prevention of Smishing Attacks
A Naive Bayes classifier with NoSlang dictionary normalization detects smishing messages at 96.2 percent accuracy on a hand-built subset of the SMS Spam Collection.