AI-Powered Spam Detection: An Intelligent Approach to Secure Digital Communication

Abstract: In today's digital landscape, the detection and filtering of unwanted communications, known as spam, are an integral part of protecting cyber security and trust in users. This paper presents an AI spam detection system that uses state-of-the-art machine learning (ML) and natural language processing (NLP) methods to identify and filter bad or irrelevant online messages. The system analyzes text patterns, frequency of suspicious words, and sender information. We performed a comparative study with three classifiers, Naive Bayes, Support Vector Machine (SVM), and a Neural Network model, to differentiate spam and valid messaging content. The models are trained on large labeled datasets and show good accuracy for classifying text and identifying various threats such as phishing attacks, online scams, and unsolicited marketing messages. Artificial intelligence can be applied to improve spam filtering in real-time, and is a scalable and intelligent method to the difficult problems in digital communication today

Keywords: AI-driven spam detection framework, cyber security, Machine learning, Natural language processing, Naive Bayes, Support Vector Machine, Neural Network model.