Smishing and Cybersecurity : Why Traditional Defense are No Longer Available

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

Abstract

Society’s current cybersecurity systems have demonstrated their inability to stop evolving attacks that include smishing operations. Mobile devices provide access for the phishing technique known as smishing which takes advantage of human psychology to evade standard security measures through messaging apps. This article studies why current cybersecurity methods fail to combat smishing while demonstrating the necessity of developing complex defensive approaches to protect people and institutions from rising malicious text message attacks.

Introduction

The digital transformation enabled entirely new ways people interact while it changed both professional operations and business activities. New cybercrime forms have appeared since the digital era began and smishing has proven to be one of the most damaging attacks. Users increasingly fall victim to smishing attacks because hackers exploit text messages with deceptive content to obtain sensitive information and malware installation among victims.[1] Financial and administrative frameworks like firewalls and antivirus software and email filters demonstrate limited capability when protecting against the distinctive attacks that smishing represents. The article explores why conventional defense solutions no longer work while presenting solutions that confront this developing cyber threat.

Why Traditional Defenses Fall Short

The current security measures emerged to counter the threats from earlier times when attackers utilized emails and malicious websites. The different operating environment of smishing diminishes the effectiveness of traditional security measures. Several important elements demonstrate that traditional security measures have become insufficient in modern times.

1. Lack of Visibility into Messaging Platform[2]:

The current security tools that use firewalls and email filters do not provide detection or monitoring capabilities for SMS or messaging application traffic. Because of its stealth nature the actual operation of smishing attacks remains unseen.

2. Human Centric Nature of Smishing[3]

The human psyche remains the target of smishing attacks because the attackers purposely create urgent situations of fear and curiosity to deceive victims. Traditional security measures fail to deal with human attackers since they concentrate on technical vulnerabilities.

A diagram of a crime

AI-generated content may be incorrect.

3. Encryption on Messaging Platforms[4]:

User privacy receives protection through end-to-end encryption in popular messaging apps that include WhatsApp and Signal. The security benefit of this protection limits the ability of cybersecurity tools to search for harmful content in messages.

4. Rapid Evolution of Tactics:

Scammers keep modifying their fraudulent methods to escape security systems. Techniques such as shortening URLs as well as implementing voice communication and QR codes allow scammers to circumvent standard security measures and blacklist protocols.

5. Limited User Awareness[4]:

Most users remain unaware about the security dangers associated with smishing attempts because they lack recognition skills for identifying fraudulent communications. Current defense strategies fail to train users about identifying suspicious activity and they lack the tools to report such incidents.

The Consequences of Inadequate Defenses

Traditional cybersecurity defenses fail to challenge smishing effectively which creates severe adverse effects for users both personally and in their professional organizations. The consequences for people who become victims of these attacks include financial costs combined with stolen identities and unauthorized account entry[5]. Businesses face significant consequences when attacked with smishing by experiencing data breaches and incurring both reputation damage and lawsuits from the authorities. The increasing number of smishing incidents weakens user confidence in digital communications because they become reluctant to approach genuine messages or deals.

The Need for Advanced, Multi-Layered Strategies

A new cybersecurity model must replace traditional security measures to fight smishing because standard defenses do not effectively confront these present threats. Several essential elements constitute a multi-layered strategy as follows:

1. AI and Machine Learning:

Real-time detection and blocking of current smishing attempts become possible using advanced technologies including Artificial Intelligence and machine learning which examine messaging system patterns. These defensive tools demonstrate capability to adjust their strategies since tactics evolve which results in improved security protection.

2. User Education and Awareness:

Users need to be up to date on the risks of smishing and be able to spot questionable text messages. When companies hold training sessions in addition to phishing test sessions and educational programs about cyber risks, users take on the role of the main session defenders.

3. Enhanced Monitoring of Messaging Platforms:

Security solutions need to add SMS and messaging application monitoring features to their evolution. The solution includes creating security APIs with platform providers and launching on-device scanning solutions.

4. Multi-Factor Authentication (MFA):

Multi-Factor Authentication helps diminish the effects of smishing attacks through its safety-enhancing security measures. Scammers can access login credentials but still fail to access accounts because they require the second factor in addition to these credentials.

5. Collaboration and Information Sharing:

States and corporate entities together with cybersecurity organizations need to create a system where they exchange threat data while building standard protocols to fight off smishing attacks. The global challenge benefits from successful public-private collaboration models for its resolution.

Conclusion

traditional cybersecurity methods prove inadequate for combatting contemporary threats that society faces because of smishing’s popularity growth. A new method is essential in stopping scammers from exploiting messaging platforms because it must integrate modern technology solutions with education for users and joint industry action. Organizations and people can combat smishing attacks and other evolving technical threats with the help of a multi-tiered protection strategy. Because the globe is becoming more interconnected by the day, modern cybercriminals need individuals to constantly innovate and monitor their activities. Since smishing threatens to increase the risks to our digital lives, we must act right away.

References

  1. M. L. Rahman, D. Timko, H. Wali, and A. Neupane, “Users Really Do Respond To Smishing,” in Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy, in CODASPY ’23. New York, NY, USA: Association for Computing Machinery, Apr. 2023, pp. 49–60. doi: 10.1145/3577923.3583640.
  2. A. Nahapetyan et al., “On SMS Phishing Tactics and Infrastructure,” in 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA: IEEE, May 2024, pp. 1–16. doi: 10.1109/SP54263.2024.00169.
  3. M. Mutlutürk, M. Wynn, and B. Metin, “Phishing and the Human Factor: Insights from a Bibliometric Analysis,” Information, vol. 15, no. 10, p. 643, Oct. 2024, doi: 10.3390/info15100643.
  4. A. K. Jain and B. B. Gupta, “A survey of phishing attack techniques, defence mechanisms and open research challenges,” Enterp. Inf. Syst., vol. 16, no. 4, pp. 527–565, Apr. 2022, doi: 10.1080/17517575.2021.1896786.
  5. “Automatic Smishing Detection System with Feedback Loops.” Accessed: Feb. 17, 2025. [Online]. Available: https://www.researchsquare.com
  6. Gupta, B. B., Gaurav, A., & Arya, V. (2024). Fuzzy logic and biometric-based lightweight cryptographic authentication for metaverse security. Applied Soft Computing, 164, 111973.
  7. Upadhyay, U., Kumar, A., Sharma, G., Sharma, S., Arya, V., Panigrahi, P. K., & Gupta, B. B. (2024). A systematic data-driven approach for targeted marketing in enterprise information system. Enterprise Information Systems, 18(8), 2356770.
  8. Zhang, J., Li, X., Vijayakumar, P., Liang, W., Chang, V., & Gupta, B. B. (2024). Graph sparsification-based secure federated learning for consumer-driven Internet of Things. IEEE Transactions on Consumer Electronics.
  9. Katiyar A. (2024) Social Engineering Phishing Detection, Insights2Techinfo, pp.1

Cite As

Karthik V. (2025) Generative AI and Phishing : Smishing and Cybersecurity : Why Traditional Defense are No Longer Available, Insights2techinfo pp.1

84920cookie-checkSmishing and Cybersecurity : Why Traditional Defense are No Longer Available
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