Unmasking Deception: Artificial Neural Networks in Smishing Detection for Cyber Security Fortification

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : T.V. Mini, Jomy John, P.D. Siji

10.14445/23488549/IJECE-V11I7P114

How to Cite?

T.V. Mini, Jomy John, P.D. Siji, "Unmasking Deception: Artificial Neural Networks in Smishing Detection for Cyber Security Fortification," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 7, pp. 141-149, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I7P114

Abstract:

Smishing is a form of social engineering that employs text messaging to mislead people into revealing confidential information or performing malicious actions. It has become a prevalent and sophisticated cyber threat. The rise of smishing (SMS phishing) poses a significant threat to cyber security, demanding advanced detection and classification methods to safeguard users and organizations. As a deceptive technique targeting mobile users through text messages, smishing requires proactive defense mechanisms. This paper proposes an Artificial Neural Networks (ANNs) model for smishing detection and classification to bolster cyber security. This paper outlines a proposed framework comprising four distinct modules designed for smishing detection and classification. Through simulation, the framework demonstrated exceptional performance, attaining an accuracy rate of 97.66%. Comparative analysis against various machine learning models underscored the superiority of the proposed approach. As mobile devices continue to be integral to daily communication, the implementation of ANN-based solutions serves as a vital component in fortifying cyber defenses, ensuring the security and privacy of individuals and organizations in an increasingly interconnected digital landscape.

Keywords:

Cyber threats, Text messages, Spam SMS, Phishing, Smishing, Cyber Security, Neural Network, Back propagation algorithm.

References:

[1] Ross Anderson et al., Measuring the Cost of Cybercrime, The Economics of Information Security and Privacy, Springer, Berlin, Heidelberg, pp. 265-300, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[2] What Are the Most Common Cyber Attacks?, 2022. [Online]. Available: https://www.cisco.com/c/en_in/products/security/common-cyberattacks.html#~typesof-cyber-attacks
[3] Mahmoud Khonji, Youssef Iraqi, and Andrew Jones, “Phishing Detection: A Literature Survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2091-2121, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Worldwide Texting Statistics, 2018. [Online]. Available: https://shso.vermont.gov/content/worldwide-texting-statistics
[5] Sandhya Mishra, and Devpriya Soni, “Smishing Detector: A Security Model to Detect Smishing through SMS Content Analysis and URL Behavior Analysis,” Future Generation Computer Systems, vol. 108, pp. 803-815, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Jae Woong Joo et al., “S-Detector: An Enhanced Security Model for Detecting Smishing Attack for Mobile Computing,” Telecommunication Systems, vol. 66, pp. 29-38, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ankit Kumar Jain, Sumit Kumar Yadav, and Neelam Choudhary, “A Novel Approach to Detect Spam and Smishing SMS Using Machine Learning Techniques,” International Journal of E-Services and Mobile Applications, vol. 12, no. 1, pp. 21-38, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Caner Balim, and Efnan Sora Gunal, “Automatic Detection of Smishing Attacks by Machine Learning Methods,” 1 st International Informatics and Software Engineering Conference, Ankara, Turkey, pp. 1-3, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Oluwatobi Noah Akande et al., “Development of a Real Time Smishing Detection Mobile Application Using Rule Based Techniques,” Procedia Computer Science, vol. 199, pp. 95-102, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Heider A.M. Wahsheh, and Mohammed S. Al-Zahrani, “Lightweight Cryptographic and Artificial Intelligence Models for Anti-Smishing,” Proceedings of International Conference on Emerging Technologies and Intelligent Systems, pp. 483-496, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Gunikhan Sonowal, and K.S. Kuppusamy, “SmiDCA: An Anti-Smishing Model with Machine Learning Approach,” The Computer Journal, vol. 61, no. 8, pp. 1143-1157, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Diksha Goel, and Ankit Kumar Jain, “Smishing-Classifier: A Novel Framework for Detection of Smishing Attack in Mobile Environment,” Smart and Innovative Trends in Next Generation Computing: Third International Conference, Dehradun, India, pp. 502- 512, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Neda Abdelhamid, Fadi Thabtah, and Hussein Abdel-Jaber, “Phishing Detection: A Recent Intelligent Machine Learning Comparison Based on Models’ Content and Features,” 2017 IEEE International Conference on Intelligence and Security Informatics, Beijing, China, pp. 72-77, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ping Yi et al., “Web Phishing Detection Using a Deep Learning Framework,” Wireless Communications and Mobile Computing, vol. 2018, no. 1, pp. 1-9, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jian Mao et al., “Phishing-Alarm: Robust and Efficient Phishing Detection via Page Component Similarity,” IEEE Access, vol. 5, pp. 17020-17030, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Tiago A. Almeida, Jose Maria G. Hidalgo, and Akebo Yamakami, “Contributions to the Study of SMS Spam Filtering: New Collection and Results,” Proceedings of the 11th ACM Symposium on Document Engineering, Mountain View California USA, pp. 259-262, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[17] S. Vijayarani, M.J. Ilamathi, and M. Nithya, “Preprocessing Techniques for Text Mining - An Overview,” International Journal of Computer Science & Communication Networks, vol. 5, no. 1, pp. 7-16, 2015.
[Google Scholar] [Publisher Link]
[18] M. Soranamageswari, and C. Meena, “Statistical Feature Extraction for Classification of Image Spam Using Artificial Neural Networks,” 2010 Second International Conference on Machine Learning and Computing, Bangalore, India, pp. 101-105, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Jing Li et al., “Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement,” Advances in Computer Science and Information Engineering, vol. 2, pp. 553-558, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Back Propagation Process in Deep Neural Network, 2022. [Online]. Available: https://www.javatpoint.com/pytorch-backpropagation-process-in-deep-neural-network
[21] Robert Susmaga, “Confusion Matrix Visualization,” Proceedings of the International Intelligent Information Processing and Web Mining, Zakopane, Poland, pp. 107-116, 2004.
[CrossRef] [Google Scholar] [Publisher Link]