AI-Driven Enhancement of Spam Detection in SMS and Email Using AWS Leveraging Deep Spam Model
DOI:
https://doi.org/10.63682/jns.v14i15S.3865Keywords:
SMS spam detection, email spam detection, Machine Learning, AWS Lambda, Text classification methodsAbstract
This study proposes a revolutionary strategy to enhance spam detection in SMS and email communications by integrating the powerful AWS cloud architecture with cutting- edge artificial intelligence (AI) approaches. The project seeks to produce a highly effective system that can discriminate between incoming messages that are spam and those that are legitimate (ham) by utilizing machine learning models that were created on the customized Amazon Sage Maker platform. The solution is deliberately developed and incorporates crucial pieces including AWS Lambda functions, Simple Email Service (SES), S3 buckets for data storage, and the trustworthy MXNet framework for model training and deployment. The suggested solution contains an extensive procedure that combines expensive pre processing, complex feature extraction approaches, hard model training processes, and seamless real- time message classification. The study's experimental findings clearly illustrate the remarkable efficacy of the offered strategy in accurately identifying and categorizing spam messages, considerably enhancing communication security and dependability overall. This research fulfills the highest conference standards, as it includes a full investigation of the topic coupled with practical application and real-world repercussions
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