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CyberTracker: A Web Portal for Cyber Fraud Monitoring and Interactive Querying Using Lightweight Language Models

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
A. Vikasni
B. Jagathsri
A. M. Abirami
Published in
Data Science and Network Engineering
Publisher
Springer Nature Switzerland

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Abstract

CyberTracker is a cutting-edge platform designed to revolutionize cybercrime monitoring and analysis. This chapter delves into the system's architecture, which integrates real-time data scraping, machine learning-based classification, and interactive visualization tools. The platform's natural language interface, powered by lightweight language models like Gemini and GPT, allows users to query the system intuitively and conversationally. The text also explores the system's performance evaluation, including accuracy, latency, and scalability, as well as its resilience to adversarial threats. Furthermore, it discusses the potential for future enhancements, such as multilingual support and anomaly detection capabilities. By bridging the gap between raw cyber threat data and actionable intelligence, CyberTracker contributes to situational awareness, proactive defense strategies, and strengthened cyber resilience. The chapter concludes with a discussion on the system's potential for future expansions and its applicability to broader security and intelligence contexts.
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previous chapter Unsupervised Pattern Discovery in Cyber Incidents Using Principal Component Analysis K-Means DBSCAN and Isolation Forest
Title
CyberTracker: A Web Portal for Cyber Fraud Monitoring and Interactive Querying Using Lightweight Language Models
Authors
A. Vikasni
B. Jagathsri
A. M. Abirami
Copyright Year
2026
Book
Data Science and Network Engineering

Print ISBN: 978-3-032-07734-9

Electronic ISBN: 978-3-032-07735-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-07735-6_28
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