2024/12/31 by Diksha Goel, Goel, Diksha
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2501.00260
openalex publication_date 2024/12/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Phishing is an online identity theft technique where attackers steal users personal information, leading to financial losses for individuals and organizations. With the increasing adoption of smartphones, which provide functionalities similar to desktop computers, attackers are targeting mobile users. Smishing, a phishing attack carried out through Short Messaging Service (SMS), has become prevalent due to the widespread use of SMS-based services. It involves deceptive messages designed to extract sensitive information. Despite the growing number of smishing attacks, limited research focuses on detecting these threats. This work presents a smishing detection model using a content-based analysis approach. To address the challenge posed by slang, abbreviations, and short forms in text communication, the model normalizes these into standard forms. A machine learning classifier is employed to classify messages as smishing or ham. Experimental results demonstrate the model effectiveness, achieving classification accuracies of 97.14% for smishing and 96.12% for ham messages, with an overall accuracy of 96.20%.