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Phishing detection in SMS using NLP

 Department: Computer Science  
 By: usericon olagok01  

 Project ID: 8971
   Rating:  (5.0) votes: 1
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   Price:₦4000
Abstract
The pervasive use of mobile devices has made SMS a prime vector for phishing attacks, known as smishing, posing significant threats to personal and financial security. Traditional defense mechanisms, such as blacklisting and rule-based filters, are increasingly ineffective against evolving social engineering tactics and the concise, informal nature of SMS. This research addresses this critical security gap by designing and implementing an intelligent system for proactive smishing detection using Natural Language Processing (NLP) and Machine Learning (ML). The study employs a structured methodology involving data collection from public repositories, rigorous text preprocessing (tokenization, stopword removal, lemmatization), and feature extraction using TF-IDF and Word Embeddings. Various ML classifiers, including Random Forest, Support Vector Machine, and Logistic Regression, were evaluated, with the Random Forest model achieving a remarkable accuracy of 99.2% on a balanced dataset. The proposed system is conceptualized within the operational context of a major telecommunications network, outlining a robust architecture for real-time message classification that outputs a phishing probability score and a binary label. This research conclusively demonstrates that an NLP-driven approach can effectively identify deceptive linguistic patterns in SMS, thereby enabling proactive detection, enhancing user protection, optimizing security resource allocation, and contributing a valuable framework to the cybersecurity domain. The study recommends the integration of such models into telecom infrastructure and messaging platforms to mitigate the growing threat of smishing attacks....
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