Cyberguard: An Ai Chatbot for Phising and Scam Detection
Keywords:
Cybersecurity awareness, phishing detection, scam prevention, conversational AI, rule-based NLP, multi-source reputation analysisAbstract
CyberGuard is a lightweight cybersecurity assistant designed to help non-expert users identify phishing, malicious links, and scam messages in real time. Instead of relying on a single data source, it aggregates information from multiple threat intelligence feeds to verify URL and caller reputations. This is combined with rule-based NLP to analyze messages for suspicious keywords. The system focuses on user empowerment through clear, actionable guidance and educational prompts that improve digital safety habits. With an accessible interface, text and voice response options, and simple risk explanations, it is designed for ease of use. Its practical, multi-layered approach improves the detection of digital threats and builds user confidence. The platform's low-cost, modular design, which includes utility tools like WHOIS and ping checks, makes it an ideal solution for academic environments and everyday users seeking to enhance their online security.
Downloads
References
Arikkat, D. R., et al. "IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 644–651. IEEE, 2024. https://doi.org/10.1109/CICN63059.2024.10869698
Rosari, H. S., Girinoto, R. N. Yasa, N. Qomariasih, and R. A. Wijayanti. "SiJesi: Large Language Model Chatbot with Augmented Retrieval Approach Generation and Prompt Engineering." In 2024 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pp. 1–13. IEEE, 2024. https://doi.org/10.1109/CENIM64118.2024.10834490
Bhanushali, M., H. Parekh, Y. Mane, and R. Mistry. "TAKA Cybersecurity Chatbot." In 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 1–7. IEEE, 2023. https://doi.org/10.1109/ICACTA58199.2023.10393589
Mishra, R., G. Varshney, and S. Singh. "Jailbreaking Generative AI: Empowering Novices to Conduct Phishing Attacks." In 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks – Supplemental Volume (DSN-S), pp. 251–252. IEEE, 2025. https://doi.org/10.1109/DSN-S65097.2025.00054
Vishwanath, A. "Habitual Facebook Use and Its Impact on Getting Deceived on Social Media." Journal of Computer-Mediated Communication 20, no. 1 (2015): 83–98. https://doi.org/10.1111/jcc4.12100
Proofpoint. "2024 State of the Phish Report." Proofpoint Inc., Sunnyvale, CA, 2024.
Chiew, K. L., K. S. C. Yong, and C. L. Tan. "A Survey of Phishing Attacks: Their Types, Vectors and Technical Approaches." Expert Systems with Applications 106 (2018): 1–20. https://doi.org/10.1016/j.eswa.2018.03.050
Wash, R., and M. E. Newman. "Individual Differences in Susceptibility to Online Influence." In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1–10. ACM, 2013. https://doi.org/10.1145/2470654.2470714
Hamad, S., and T. Yeferny. "A Chatbot for Information Security." IJCSNS International Journal of Computer Science and Network Security 20, no. 4 (2020): 289–291.
DeCusatis, C., et al. "A Cybersecurity Awareness Escape Room using Gamification Design Principles." In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0765–0770. IEEE, 2022. https://doi.org/10.1109/CCWC54503.2022.9720826
McGrath, D. K., and M. Gupta. "Behind Phishing: An Examination of Phisher Modi Operandi." LEET 8 (2008): 4.
Le Page, A., et al. "URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection." arXiv preprint arXiv:1802.03162 (2018).
Shirazi, H., S. Muramudalige, I. Ray, and A. Jayasumana. "Improved Phishing Detection Using Stacked Generalization." In 2020 IEEE 45th Conference on Local Computer Networks (LCN), pp. 1–9. IEEE, 2020. https://doi.org/10.1109/LCN48667.2020.9314774
Almomani, A., B. B. Gupta, S. Atawneh, A. Meulenberg, and E. Almomani. "A Survey of Phishing Email Filtering Techniques." IEEE Communications Surveys & Tutorials 15, no. 4 (2013): 2070–2090. https://doi.org/10.1109/SURV.2013.030713.00020
Verizon. "2024 Data Breach Investigations Report." Verizon Communications Inc., New York, NY, 2024.
Srinivasan, S., and S. Ravi. "Machine Learning Approaches for Phishing Website Detection: A Systematic Review." Journal of Cybersecurity and Privacy 3, no. 2 (2023): 145–172. https://doi.org/10.3390/jcp3020009
Abbasi, A., Z. Zhang, D. Zimbra, H. Chen, and J. F. Nunamaker Jr. "Detecting Fake Websites: The Contribution of Statistical Learning Theory." MIS Quarterly 34, no. 3 (2010): 435–461. https://doi.org/10.2307/25750686
Gupta, S., A. Singhal, and A. Kapoor. "A Literature Survey on Social Engineering Attacks: Phishing Attack." In 2016 International Conference on Computing, Communication and Automation (ICCCA), pp. 537–540. IEEE, 2016. https://doi.org/10.1109/CCAA.2016.7813778
Ramesh, G., I. Krishnamurthi, and K. S. S. Kumar. "An Efficacious Method for Detecting Phishing Webpages Through Target Domain Identification." Decision Support Systems 61 (2014): 12–22. https://doi.org/10.1016/j.dss.2013.10.002
Ahmad, T. "Corona Virus (COVID-19) Pandemic and Work from Home: Challenges of Cybercrimes and Forensics." SSRN Electronic Journal (2020). https://doi.org/10.2139/ssrn.356883
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
