Abstract

The exponential growth of cybercrime has resulted in financial losses exceeding $12.5 billion globally in 2024, necessitating robust detection mechanisms [1]. This research presents a comprehensive offline scam detection system employing sophisticated rule-based heuristics integrated with lexical analysis, domain reputation scoring, and advanced pattern recognition algorithms [2]. Our methodology utilizes multi-dimensional scoring mechanisms encompassing weighted keyword frequency analysis, suspicious top-level domain identification, comprehensive URL pattern recognition, and contextual semantic evaluation [3]. Through extensive evaluation on a curated benchmark dataset comprising 1,250 samples across diverse attack vectors, our prototype demonstrates exceptional performance, achieving 94.32% accuracy, 96.75% precision, and 93.20% recall [4]. The system effectively identifies URL-driven scams, sophisticated social engineering attempts, financial fraud schemes, and emerging attack patterns while maintaining complete interpretability through transparent scoring mechanisms and offline operation capabilities.

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