Enhancing Smishing Detection A Deep Learning Approach for Improved Accuracy and Reduced False Positives

Aim:

          The aim of this work is to explore and develop advanced methods for enhancing the detection and prevention of smishing attacks. This involves utilizing cutting-edge technologies such as machine learning, artificial intelligence, and behavioral analysis to identify and block fraudulent SMS messages, protecting users from financial and personal data theft. The goal is to create more effective, real-time detection systems to mitigate the growing threat of smishing attack

Abstract:

       Smishing, a form of SMS phishing, has emerged as a significant threat in the landscape of cybercrime, targeting individuals through deceptive text messages designed to steal personal and financial information. Despite the growing prevalence of these attacks, traditional security systems often struggle to detect and mitigate smishing effectively. This paper aims to explore innovative approaches to enhancing smishing detection, focusing on the integration of machine learning algorithms, natural language processing (NLP), and advanced behavioral analysis techniques. By leveraging these technologies, we propose a framework capable of identifying malicious SMS messages with greater accuracy and speed. Additionally, the study evaluates existing detection methods, highlighting their limitations and offering recommendations for improvement. The goal of this research is to provide a more robust defense against smishing, reducing the risk of data breaches and financial losses for individuals and organizations alike. Ultimately, this work seeks to advance the field of mobile security and contribute to the development of more intelligent and proactive systems to combat SMS-based cyber threats

Proposed System:

        Inspired by advancements in machine-learning techniques coupled with promising results obtained in message classification. This study proposes a machine-learning based model to classify Smishing text messages targeting mobile money users. Machine-learning techniques are advantageous to other techniques as they can detect both known malware and obfuscated malware. The contributions of this study, organized and carried out under a real-world Smishing dataset collected from mobile money users. The proposed model would save mobile money users from financial losses they incur as a result of social engineering attacks that keep on utilizing local dialects that are less studied.

Advantage:

       Smishing messages targeting mobile money users use words in a well-orchestrated pattern and a mobile number to receive electronic money from a victim. The proposed model has a high accuracy score compared to general Smishing detection models. A lower accuracy for the  dataset can be attributed to the fact that the formation of words and sentences in the language is very different from other well studied languages such as English, which has been extensively used by other researchers.