Volume no :
9 |
Issue no :
1
Article Type :
Scholarly Article
Author :
G Bharadwaj Muneendra, Vignesh K, G Sampath
Published Date :
April, 2025
Publisher :
Journal of Theoretical and Computational Advances in Scientific Research (JTCASR)
Page No: 1 - 12
Abstract : The rapid adoption of chatbots by organizations to efficiently manage user queries has brought significant advancements, but it has also introduced new risks. Traditionally, before the integration of machine learning (ML) and artificial intelligence (AI), phishing prevention relied on manual techniques such as blacklists, rule-based filters, and heuristic analysis, which were often slow and insufficient against evolving threats. The primary issue was the manual nature of these systems, which struggled to keep up with the sophisticated tactics used by malicious entities, leading to the exploitation of chatbots for phishing attacks. This challenge highlighted the need for more intelligent and adaptive security measures. The objective of this research is to design, develop, and integrate a self-defensive chatbot capable of identifying and neutralizing phishing attempts by inspecting URLs embedded in user interactions. The motivation behind this study stems from the increasing incidents where chatbots are manipulated to deliver phishing links that, when clicked, install malicious software to steal sensitive data such as cookies and session passwords. This is particularly concerning for sectors like banking and finance, where compromised data can lead to significant user losses. The proposed system leverages machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Decision Tree, to create a robust model trained on the PHISH TANK URL dataset. This model can accurately distinguish between normal and malicious URLs in real-time, thereby enhancing the security of chatbot interactions. By evaluating each algorithm's performance through metrics such as accuracy, precision, recall, F-score, and confusion matrices, the system ensures optimal phishing detection capabilities. This integration is demonstrated through a dummy banking application where the chatbot processes user queries, employing natural language processing (NLP) techniques to extract and safeguard sensitive information.
Keyword Phishing Detection, Chatbot Security, Machine Learning, URL Classification, Natural Language Processing, Cybersecurity in Banking
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