By: Vanna karthik; Vel Tech University, Chennai, India
Abstract
SMS phishing threats have shifted from containing a local scale to affecting people internationally because cybercriminals now use advanced techniques to make fraudulent messages appealing for speakers of diverse languages. Modern scammers exploit cultural differences and language challenges to develop persuasive smishing messages which compromise victims from any nation. This study investigates the global expansion of smishing and discusses the procedures scammers utilize when attacking audiences who do not speak English together with the challenges these attacks create for both personal and professional groups. The document presents operative measures for protecting against multilingual smishing security incidents.
Introduction
Text message deception using the form of smishing has turned into an important security threat that aims to trick victims into disclosing personal data or malware installations. Technical progress along with worldwide integration allowed scammers to target non-English populations beginning from the original English-speaking audience. Smishing attackers now execute their operations across various languages to deceive people from different cultural backgrounds[1]. Smishing has become an increasingly widespread security threat because scammers now use linguistic and cultural differences to boost their success in carrying out attacks.
A comprehensive study investigates smishing globalization by evaluating how criminals adjust their methods to reach diverse linguistic groups alongside the protection strategies for both people and organizations.
The Globalization of Smishing
Smishing globalization advances thanks to three factors which combine smartphone adoption on a large scale with translation capability tools along with the growing international economic connections. Smishing continues to spread globally based on these critical trends:
1. Exploiting Language Barriers:
Smishing remains less familiar to people who do not know English, so they become more prone to falling victim to these attacks. Targeted victims receive translated and multilingual smishing messages through automated chatbot systems which makes them more likely to fall victim to scams[2].
2. Cultural Adaptation:
The messages used by scammers are designed to match the preferred cultural standards and significant local events in their target areas. During festive seasons as well as holiday times smishing attackers utilize messages that duplicate local traditions and advertise deceptive discounts on popular goods[2].
3. Targeting Emerging Markets
Rapid smartphone adoption in India Brazil along with Nigeria produces a high volume of smishing targets due to weak cybersecurity understanding within these nations. Illegal actors take advantage of deficient security regulations alongside low public knowledge in these areas.
4. Leveraging Global Platforms
Across the world messaging applications like WhatsApp, WeChat and Telegram enable scammers to communicate with audiences who speak different languages[3]. These apps have insufficient security protocols which make them perfect tools for conducting smishing scams.
Tactics Used in Multilingual Smishing
Modern criminals use advanced strategies to approach speakers from multiple languages. Four primary methods used in this type of fraud strategy include:
1. Localized Lures
The content of smishing messages matches the region’s language at the same time it includes local currency information and tries to use customary local examples. Smishing messages in Japan will use yen values coupled with Japanese brands while Spanish messages will display euro values together with Spanish tradition references.
2. Fake Government Alerts
The crooks pretend to represent governmental institutions together with local government bodies when they contact victims in their native tongue. The fake communication demands money payment for legal compliance purposes or asks users to validate their personal identification before legal punishment occurs[4].
3. Impersonating Local Businesses
When attempting to deceive victims the attackers pretend to operate businesses that locals frequently use including financial institutions, telecommunications companies and online stores[4]. The pretense of an impersonation attack in Germany under the guise of Deutsche Bank differs from the identical tactic as Flipkart in India.
4. Multilingual Chatbots
Scammers benefit from using advanced chatbots that use generative AI technology to process multiple languages so they can expand their operations by pursuing many different audiences[5], [6].
Challenges in Combating Multilingual Smishing [7]
The worldwide spread of smishing creates multiple difficulties for people as well as organizations and security professionals.
1. Lack of Awareness:
Non-English-speaking individuals typically remain unaware about smishing and its risks because of which they become vulnerable to malicious attacks.
2. Language Barriers for Detection:
Traditional spam filters together with detection systems are built to detect English-sourced messages which creates issues when examining messages in other languages.
3. Cross-Border Jurisdiction Issues:
The origin of smishing attacks typically begins in one country while the targeted victims exist in different jurisdictions which makes both prevention and criminal prosecution challenging.
4. Rapidly Evolving Tactics:
Scammers act proactively to enhance their techniques for avoiding detection tools while discovering fresh methods of attacking systems.
Conclusion
The worldwide expansion of smishing brought notable changes to the cyber security domain because attackers now aim at multiple language speakers to access new victim groups while boosting their attack effectiveness. People and organizations need to establish preventative measures to protect themselves against upcoming sophisticated widespread smishing campaigns. Our ability to provide protection from multilingual smishing depends on awareness creation alongside advanced detection technologies and international cooperation between different entities.
References
- R. Saeki, L. Kitayama, J. Koga, M. Shimizu, and K. Oida, “Smishing Strategy Dynamics and Evolving Botnet Activities in Japan,” IEEE Access, vol. 10, pp. 114869–114884, 2022, doi: 10.1109/ACCESS.2022.3217795.
- A. U. Patience, “Globalisation and Language Barriers: The Translation Perspective,” 2016.
- M. J. Masoodi and S. Andrey, “Understanding the Use of Private Messaging Apps in Canada and Links to Disinformation,” IEEE Technol. Soc. Mag., vol. 41, no. 3, pp. 58–70, Sep. 2022, doi: 10.1109/MTS.2022.3197115.
- R. Kohilan, H. E. Warakagoda, T. T. Kitulgoda, N. Skandhakumar, and N. Kuruwitaarachchi, “A Machine Learning-based Approach for Detecting Smishing Attacks at End-user Level,” in 2023 IEEE International Conference on e-Business Engineering (ICEBE), Nov. 2023, pp. 149–154. doi: 10.1109/ICEBE59045.2023.00042.
- M. Hasal, J. Nowaková, K. Ahmed Saghair, H. Abdulla, V. Snášel, and L. Ogiela, “Chatbots: Security, privacy, data protection, and social aspects,” Concurr. Comput. Pract. Exp., vol. 33, no. 19, p. e6426, 2021, doi: 10.1002/cpe.6426.
- P. Pappachan, M. Moslehpour, and M. Rahaman, “Beyond Neural Networks: Enriching ChatGPT with Rule-Based Approaches,” vol. 05, 2022.
- E. Ramanujam, K. Shankar, and A. Sharma, “A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection,” in High Performance Computing, Smart Devices and Networks, R. Malhotra, L. Sumalatha, S. M. W. Yassin, R. Patgiri, and N. B. Muppalaneni, Eds., Singapore: Springer Nature, 2024, pp. 525–536. doi: 10.1007/978-981-99-6690-5_40.
- Zheng, Q., Wang, X., Khan, M. K., Zhang, W., Gupta, B. B., & Guo, W. (2017). A lightweight authenticated encryption scheme based on chaotic scml for railway cloud service. IEEE Access, 6, 711-722.
- Al-Ayyoub, M., AlZu’bi, S., Jararweh, Y., Shehab, M. A., & Gupta, B. B. (2018). Accelerating 3D medical volume segmentation using GPUs. Multimedia Tools and Applications, 77, 4939-4958.
- Gupta, S., & Gupta, B. B. (2018). XSS-secure as a service for the platforms of online social network-based multimedia web applications in cloud. Multimedia Tools and Applications, 77, 4829-4861.
Cite As
Karthik. V. (2024) Smishing Goes Global : How Scammers are Targeting Multilingual Audiences, Insights2Techinfo, pp. 1