SMART SHIELD: A REAL-TIME, LANGUAGE-AWARE SYSTEM FOR SMS SPAM DETECTION

The proliferation of SMS spam presents a significant challenge in modern mobile communication, resulting in user dissatisfaction, reduced trust in messaging services, and heightened security vulnerabilities such as phishing attacks and data breaches. Traditional spam detection methods often fall short in handling the dynamic and evolving nature of spam content, especially in real-time environments where speed and accuracy are critical. This study introduces a comprehensive, real-time SMS spam filtering system designed to deliver high performance with minimal latency.The proposed system leverages machine learning techniques, enhanced by advanced natural language processing (NLP) methodologies, to identify and filter spam messages with precision. The research focuses on key elements essential to real-time classification: robust data preprocessing pipelines, effective feature engineering strategies, and the selection of lightweight yet powerful machine learning algorithms suitable for deployment on mobile and cloud-based infrastructures.To address the challenge of detecting increasingly sophisticated spam content, the system incorporates NLP-based techniques such as tokenization, lemmatization, and context-aware embeddings, enabling it to capture nuanced linguistic patterns and deceptive language often used by spammers. Extensive experiments demonstrate the system’s capability to maintain high accuracy and low false-positive rates while operating within strict time constraints.Furthermore, the system is designed with adaptability and scalability in mind, supporting integration with various messaging platforms and compatibility with multiple languages. This ensures its applicability in diverse communication environments, from enterprise-level applications to individual user devices. The research underscores the potential of combining real-time processing with intelligent language understanding to offer a proactive and resilient defense against SMS spam