Abstract: Recent scenarios of companies are struggling with complex vulnerabilities. Hence, there is need of an Automated tool to overcome the longest time frame to cover the gap from forensics and cybercrime. This paper presents an innovative approach to automating Cyber Threat Intelligence processes designed to ingest, analyze, display and respond to emerging threats in real time. Formerly, CTI used to rely heavily on manual methods for collecting, interpreting and analyzing data which was not only time consuming but also prone to inefficiencies, especially when rapid information dissemination is critical.
IntelShield integrates multiple open-source intelligence feeds, does real time active network monitoring, severity scoring, triggers automated alerts via SMS using Twilio and displays threats on a user friendly dashboard. It also employs Natural Language Processing(NLP) to extract indicators of compromise (IOCs) from unstructured sources such as security blogs, reports, and dark web forums. Machine learning techniques are used to classify, prioritize, and correlate IOCs based on their severity and contextual relevance. This research highlights the transformative potential of AI-driven technologies to enhance both the speed and accuracy of CTI.
Keywords: Cyber Threat Intelligence, Automated CTI, Real Time Security, Open Source Intelligence, OSINT, CVSS, SIEM, Dashboard, Natural Language Processing (NLP), Indicators of Compromise(IOC).
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DOI:
10.17148/IJARCCE.2025.145120
