Development of an AI-Based Phishing Detection System on WhatsApp with Integration of Surabaya Local Language
Main Article Content
Abstract
Phishing is a form of cybercrime that continues to grow, especially through instant messaging platforms such as WhatsApp. Phishing messages often use fake links and persuasive language to deceive victims. This study aims to design and implement a phishing detection system based on Artificial Intelligence (AI) using two fuzzy logic approaches: Fuzzy Mamdani and Fuzzy Takagi-Sugeno-Kang (TSK), integrated into the WhatsApp platform. The system detects phishing based on several parameters, including the number of suspicious keywords, the presence of URLs, and message length, while also considering the local language context such as Indonesian, Javanese, and the Surabaya dialect. The AI model was developed using the FastAPI framework and connected to a WhatsApp bot built with Node.js for real-time communication. Implementation results show that both fuzzy models can accurately detect phishing messages in real time, with confidence levels that are consistent between manual calculations and system outputs. The system also provides automatic alerts to individual users and group chats and stores detection results in a database for further analysis. These findings highlight the effectiveness of AI-based detection systems in enhancing digital communication security, especially when adapted to regional language characteristics.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
How to Cite
References
Kaspersky, “Spam and Phishing Report 2024,” 2024. [Online]. Available: https://securelist.com
We Are Social, “Digital 2023: Global Overview Report,” 2023. [Online]. Available: https://datareportal.com
F. Zhang, M. Zhao, and T. Li, “A Survey on Phishing Attacks: Types, Techniques, and Protection,” Journal of Information Security, vol. 12, pp. 110–125, 2021.
T. Sharma, R. Singh, and M. Goel, “Phishing Detection using Hybrid AI Approaches,” IEEE Access, vol. 10, pp. 98876–98890, 2022.
N. Tarek, N. Arif, and M. J. Hossain, “Email Phishing Detection Using Fuzzy Logic-Based Decision Model,” in Proc. 24th Int. Conf. on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, Dec. 2021, pp. 1–6. doi:10.1109/ICCIT54785.2021.9689807
L. Ge, T. Wang, H. Wang, and Y. Zhao, “A TSK Fuzzy System with Gradient Descent Optimization and Rule Interpretability for Intrusion Detection,” Information Sciences, vol. 662, pp. 358–376, Apr. 2024, doi:10.1016/j.ins.2023.12.029
M. A. Wahab, A. M. Ahmed, and H. A. Hashim, “A Fuzzy Rule Interpolation Based Phishing Website Detection Approach,” Informatics, vol. 6, no. 2, p. 24, Jun. 2019, doi:10.3390/informatics6020024
M. Aburrous, M. A. Hossain, F. Thabatah, and K. Dahal, “Intelligent Phishing Website Detection System using Fuzzy Techniques,” in Proc. 3rd Int. Conf. on ICT: From Theory to Applications, Damascus, Syria: IEEE, Apr. 2008, pp. 1–6. doi:10.1109/ICTTA.2008.4530019
M. Santos and P. Silva, “AI and Cybersecurity Trends: A Survey of Applications and Challenges,” ACM Computing Surveys, vol. 55, no. 4, pp. 1–38, 2023.
R. Singh, “Deep Learning for Phishing Email Detection: A Comparative Study,” Computers & Security, vol. 116, 102698, Apr. 2022.
A. Iqbal, “Messaging Apps and Privacy Threats: Case of WhatsApp and Telegram,” Journal of Cyber Policy, vol. 8, no. 1, pp. 66–82, 2023.
S. Pandey, A. Kumar, and M. Verma, “Design and Implementation of WhatsApp Bot Frameworks for AI Applications,” IEEE Software, vol. 38, no. 6, pp. 41–47, Nov.–Dec. 2021.
H. Lee, “AI-Powered Security Solutions for Next-Generation Communication,” AI Review, vol. 45, no. 3, pp. 211–224, 2020.
R. M. Abdul-Hussein, A. H. Mohammed, and A. A. Kadhim, “Detecting Phishing Cyber Attack Based on Fuzzy Rules and Differential Evaluation,” TEM Journal, vol. 11, no. 2, pp. 543–551, May 2022, doi:10.18421/tem112-07
B. Prasetyo and A. K. Adisusilo, “Fuzzy-Based Decision Support System for Online Transaction Risk Assessment,” Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2693–2705, 2022, doi:10.3233/JIFS-212737
S. Budiprayitno, E. R. Adi, and T. R. Nugroho, “Adaptive Fuzzy Systems for Network Threats Detection,” TELKOMNIKA, vol. 18, no. 5, pp. 2474–2482, Oct. 2022
Y. Xue, E. Spero, Y. S. Koh, and G. Russello, “MultiPhishGuard: An LLM-based Multi-Agent System for Phishing Email Detection,” arXiv, May 2025.
L. S. Pentapalli, J. Salisbury, J. Riep, and K. Cohen, “A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection,” arXiv, Apr. 2025.
V. Kumaraguru, B. Mohanaprabanjan, B. Prasanth, P. Akilan, and V. Lingeshwaran, “AI-Oriented Phishing Detection System for the Strengthening of Security in Social Networks,” J. Neonatal Surg., 2025.
“WhatsPhish: WhatsApp AI Phishing Detector Chatbot,” ASEE Peer, 2022.