By: Gonipalli Bharath, Vel Tech University, Chennai, India & International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan , Gmail: gonipallibharath@gmail.com
Abstract:
With the dynamic digital development, smishing, SMS-based phishing, is an emerging significant threat in cybersecurity spheres, since it takes advantage of trust by users in messages. Smishing targets mobile users to pretend to be banks or other government-issued organizations with the goal of fraudulently obtaining confidential information from them. The paper examines appropriate ways of detecting smishing threats through URL analysis, identifying the sender, or analyzing the language tone. It also gives the prominence of the machine learning model in detecting the pattern for smishing, which also includes hybrid methods like Random Forest and Support Vector Machines, providing an accuracy level with high efficiency. While technology helps in fighting back against smishing, perfect defense depends upon user awareness and establishment of good security habits. This will require continuous technological and educational countermeasures to match the growing sophistication of fraudulent activity attacks to ensure secure mobile communications..
Introduction:
With the rapid growth of digital interactions in today’s world, cell phones have become a pivotal issue in day-to-day interaction, and SMS phishing, or, in more specific terms, smishing, has emerged as one of the biggest security threats. It plays on the basic trust which each and every one of us in-stills in our text messages with the intent to deceive people into showing information or malicious URLs. It is a subset of phishing attacks, which attacks mobile users through scams by normally sending fake messages from trusted entities such as banks, government agencies, or well-known companies. This article discusses some of the effective strategies for identification and mitigation of the threat of smishing[[1]].
Knowing Smishing: The Basics
Smishing is a portmanteau of the words “SMS” for Short Message Service and “phishing.” Unlike the general phishing that happens via emails, smishing uses SMS or text messages to deceive victims into giving away their sensitive information like log-in credentials, credit card numbers, or social security numbers. It usually provides a sense of urgency for offering something desirable-a prize or some kind of warning about an issue with an account-and instructs the target to click on some type of malignant link or send personal data to the originator[[2]]. Alongside the rise of smartphone and use of mobile internet, this makes this smishing especially dangerous because most users are accustomed to receive notifications through messaging apps and thus become less skeptical of them.
Fig[[3]]
Techniques for Detecting Smishing
URL Analysis:
Smishing attacks also can be witnessed to have some kind of suspicious URL in a message. Most of these, though appearing valid, can be linked to phony web pages designed to steal personal data. The only way such URLs would be witnessed is through anomalies: misspelling, domain names unfamiliar to the user, and suspicious subdomains. The tools for the detection of such smishing can include different URL scanners or bespoke algorithms that verify the reputation of URLs against databases of known malicious sites. Moreover, URLs containing long strings of characters or domain redirection to more than one domain can also be considered as a sign of malignancy[[4]].
Sender Identification:
The sender of the smishing message could appear to be trusted since these messages may come from what looks like a valid organization or known entity. The phone number or email address, however, could be spoofed or subtly manipulated. This could be spotted in the metadata within the phone number or SMS itself that would indicate whether it matches known numbers of legitimate businesses. The operating systems used on mobile devices are increasingly introducing features that enable customers to identify or block suspicious senders, which further intercepts smishing attempts even before they reach inboxes[[5]].
Language and Tone Analysis:
Smishing messages are replete with grammatically incorrect sentences, awkward phrasing, or the use of uncustomary structures. A significant way to identify them is based on the message content. Different NLP techniques can be conducted to scan suspicious patterns, like very urgent sentences, “Action required immediately!” or phrases forcing to respond right away without giving any logical justification. NLP can also check inconsistencies between the real content of a message and the brand identity of the sender as a way to disclose an imposter attempt[[6]].
Machine Learning Models:
Different machine learning algorithms can be trained on a labeled dataset comprising legitimate and malicious SMS messages to find patterns indicative of smishing. These models make predictions on an SMS message regarding its malicious nature based on many features such as keywords, URLs, metadata, and the tone of the message. The greater the number of data sets accumulated over time, the higher the accuracy with which machine learning models identify smishing attacks. It is realized through the application of different ensemble methods or hybrid models, which implement the best features of different algorithms. Combinations of Random Forest & Support Vector Machine or neural networks have been able to classify fraudulent and benign messages successfully..
Conclusion:
Smishing is a nascent threat that does its job perfectly by way of the trusting sense that SMS creates among users. As long as mobile phones get more into operation, so the vulnerability towards those kinds of threats increases. It requires detecting a smishing message by means of URL analysis, verification of sender, language analyses, and various machine learning algorithms. Its risk mitigation, however, would demand public awareness, two-factor authentication, SMS filtering, and other legislative measures. Whereas technology plays an important role in combating smishing, the most effective defense remains an informed and vigilant user. The general exposure to smishing can be reduced significantly by developing good security practices and using the available tools. As the digital world keeps on changing, there is a need to continuously work harder in order to stay ahead of cybercriminals and make mobile communication safer for all.
References:
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Cite As
Bharath G. (2025) Detecting Smishing: Techniques for Identifying and Mitigating SMS Phishing Threats, Insights2Techinfo, pp.1