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Rule-based Offline Scam Detection with Multi-Dimensional Scoring and Algorithmic Implementation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 42 |
| Year of Publication: 2025 |
| Authors: Mohanish Rajaneni |
10.5120/ijca2025925736
|
Mohanish Rajaneni . Rule-based Offline Scam Detection with Multi-Dimensional Scoring and Algorithmic Implementation. International Journal of Computer Applications. 187, 42 ( Sep 2025), 39-45. DOI=10.5120/ijca2025925736
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.
References
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