Event-Driven Malicious URL Extractor

Abstract

Cyber-attacks are attacks that are commonly carried out in order to obtain sensitive information or disrupt internet-based services. Recent occurrences, both internationally and locally, have shown an influx of these attacks expanding rapidly through the use of malicious URLs (Uniform Resource Locators). Traditional measures, including such blacklisting malicious URLs, make it extremely difficult to respond to such attacks in a timely and efficient manner. Most existing solutions remain restricted in terms of scalability and proactive user safeguarding in situations when freshly formed URLs are correlated with a recent event, such as Covid-19 related frauds. The proposed solution is presented with the primary aim of addressing traditional system limitations and offering an interface for users to protect themselves by detecting phishing/malicious URLs in real time. In this research, we will examine extracting user-input eventrelated keywords and leveraging NLP (Natural Language Processing) algorithms to match them with the accompanying URL (Uniform Resource Locator) token data to determine whether the URLs are malicious or benign.