Published Paper Details:

With the rapid growth of digital communication platforms such as SMS, email, and social media, cyber threats like spam, phishing, and scam messages have become increasingly sophisticated and context-driven. Traditional detection systems mainly rely on keyword matching and predefined patterns, which often fail to identify the underlying intent behind malicious communications, leading to reduced detection accuracy. This paper proposes an Intent-Aware Artificial Intelligence approach for proactive threat detection in digital communications by focusing on understanding the sender's intent using Natural Language Processing (NLP), contextual analysis, and machine learning techniques. The system analyzes linguistic patterns, behavioral cues, and contextual semantics to detect manipulation strategies such as urgency, deception, and fraudulent intent before user interaction. Additionally, the proposed model supports adaptive learning to handle evolving threat patterns and can be deployed using edge Intelligence to ensure privacy preservation and real-time processing without heavy cloud dependency. This approach aims to enhance detection accuracy, reduce false positives, and provide a more intelligent, adaptive, and human-like security mechanism for modern communication systems.