Paper Title
SMS Spam Detection System

Abstract
SMS (Short Message Service) is a widely used mobile communication medium, but it has also become a target for spam filtering to prevent SMS-based fraud and protect users from cybersecurity threats. Detecting and filtering unsolicited messages using machine learning algorithms is essential to maintain the integrity of text classification systems. This paper presents an SMS Spam Detection System that utilizes Natural Language Processing (NLP) techniques, including feature extraction and data mining, to distinguish between legitimate messages (ham) and spam messages. Various models such as Naïve Bayes, Support Vector Machines (SVM), and Deep Learning are explored, demonstrating the effectiveness of artificial intelligence (AI) in automated spam detection. Our system achieves high accuracy and shows significant potential for real-world applications, enhancing fraud prevention and strengthening automated text analysis in mobile networks.