Most spammers are constantly developing new sophisticated methods, rendering previous techniques obsolete. A thoughtful deficiency in most sms spam detection methods is lack of satisfying accuracy, reliability, low performance and comprehensibility especially when individual classifiers are used, these remains important aspects to be considered for an optimal model development. Sms spam detection using machine learning techniques is a new approach especially in ubiquitous computing devices such as mobile phones, moreover the design of short message spam detection techniques in a mobile platform is challenging task due to the non-stationary distribution of the data and the multi-lingual nature of text messages from users. It is in this background that the research proposes a multi-stage ensemble hybrid prototype sms spam detection model for a mobile environment using machine learning techniques. It involves enhanced use of pre-processing techniques, content based feature engineering techniques, multilingual natural language processing, data training and testing. The effectiveness of the proposed prototype is empirically validated using ensemble classification methods that gave an overall classification accuracy of 98.2606%.
Algorithm, Detection, Ensemble Feature engineering, Machine learning, SMS.
Andrew Kiprop Kipkebut , Moses Thiga , Elizabeth Okumu "Android Based Multi-Lingual SMS Spam Prototype Design and Development" Iconic Research And Engineering Journals Volume 6 Issue 1 2022 Page 100-105
Andrew Kiprop Kipkebut , Moses Thiga , Elizabeth Okumu "Android Based Multi-Lingual SMS Spam Prototype Design and Development" Iconic Research And Engineering Journals, 6(1)