Using Machine Learning to Detect and Prevent Smishing Attacks Effectively

By: Vanna karthik; Vel Tech University, Chennai, India

Abstract

Smishing is an evolving cyber threat relying on the exploitation of SMS messages to scam individuals into leaking sensitive information. The traditional solutions against smishing techniques have shown ineffectiveness due to evolution in these approaches. Machine learning identifies patterns or anomalies in the SMS data as a promising way of detecting smishing attacks efficiently. In order to prevent smishing, this article examines the use of machine learning algorithms that can distinguish between malicious and genuine communications. ML models can further improve security by shielding users from fraudulent schemes with NLP and supervised learning approaches. The study examined the body of research in this field, suggested a framework for using machine learning (ML) to smishing detection, and examined the effectiveness of many strategies.

Introduction

Smishing has been one of the favorite ways for cybercriminals to attack mobile users, by passing conventional security controls. In contrast to email phishing, smishing attacks tend to be more personal and urgent, which raises their success rate. The inefficiency of traditional keyword-based detection methods calls for a more robust and intelligent approach. Machine learning has emerged as a powerful tool for detecting smishing attempts by analyzing message content, sender behavior, and contextual features[1]. This paper reviews the existing research on machine learning techniques for smishing detection and proposes an effective methodology.

Understanding Smishing and Its Risks

Fraudulent messages are used to make the victim click on malicious links or reveal sensitive information through smishing. Most of the time, these are simulated communications from very well-recognized companies, which, through the urgency of the situation, deceive consumers into acting accordingly. This is a serious threat in terms of security and privacy, since SMS communication is frequent for personal conversations, banking, and verification[2].

The cybercriminals continuously improve their methods to get past traditional anti-smishing systems[3], which are mainly dependent on sender blacklists and specified keyword lists. Hence, more advanced and intelligent detection systems that can adapt immediately to new tactics are required.

Machine Learning for Smishing Detection

Machine learning offers solutions for smishing attack detection by means of text message analysis and the identification of questionable patterns. These include:

Data collection and preprocessing: SMS messages dataset is labeled as malicious or genuine and post-classification, cleaning to remove extraneous attributes is done.

Feature Extraction: Methods of NLP are used to extract relevant text-based features such as information about the sender, length of the message, and the frequency of suspicious terms[4].

Model Training and Evolution : In this article, different models are put to work, including supervised machine learning models like Random Forest and SVM, along with deep learning models like LSTM, for the classification of messages into various categories. Various trained models from a labeled dataset have been evaluated against the number of performance metrics like F1-score, accuracy, precision, and recall.

Real-time Detection and Prevention: It prevents potential smishing attacks in advance, wherein the trained model of machine learning is integrated with the mobile security application before causing any effect to mobile users.

Advantages of Machine Learning in Smishing Prevention

Compared with traditional techniques, ML-based smishing detection systems have the following benefits:
Adaptive Learning: As machine learning models consume more data, they get better over time, enabling them to identify smishing tactics that haven’t been seen before.
Increased Accuracy: ML techniques take into account a variety of criteria, which lowers false positives and negatives, in contrast to keyword-based filtering[5].
Automated Threat Detection: Security systems driven by machine learning work in real-time, detecting and thwarting attacks without the need for human interaction.
Scalability: ML algorithms are appropriate for widespread deployment across mobile networks due to their capacity to evaluate vast amounts of SMS messages effectively.

Conclusion

Machine learning uses analytics of message patterns and sender behavior to identify and prevent smishing attacks. Compared to traditional filtering methods that simply detect certain attack fingerprints, machine learning models continuously improve by adjusting to evolving attack strategies, resulting in higher accuracy and detection rates. This paper addresses the most important elements of NLP and supervised learning in fighting SMS-based phishing. Future work should focus on the refinement of model performance, real-time detection capability, and integration with ML-driven solutions into mobile security applications for all-round protection against smishing threats.

References

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Cite As

Karthik V. (2025) Using Machine Learning to Detect and Prevent Smishing Attacks Effectively, Insights2techinfo pp.1

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