An Enhanced SMS Spam Detection Framework Using Blockchain and Machine Learning
Keywords:
SMS Spam Detection, Blockchain, Machine Learning, Detection Spam FilteringAbstract
SMS spam has become a persistent, causing issues for mobile consumers throughout the world. Traditional spam detection technologies frequently fail to adequately identify and filter undesirable communications. In answer to this difficulty, this study proposes a novel architecture that combines blockchain technology and machine learning approaches to improve SMS spam detection. The framework uses blockchain's decentralised and irreversible characteristics to create a transparent and safe platform for gathering labelled SMS data from consumers. This data is then used to train machine learning models, which use a variety of strategies to increase classification accuracy, including natural language processing and ensemble methods. The efficacy and efficiency of the proposed system in identifying SMS spam while protecting user privacy are proved experimentally. The report also analyses the possible consequences of using blockchain in spam detection systems and proposes future research goals in this area. Overall, this approach marks a big step forward in combatting SMS spam and helps to continuing efforts to improve cybersecurity and consumer privacy in the mobile communication ecosystem.
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