Unraveling Threat Intelligence Through the Lens of Malicious URL Campaigns
Mahathir Almashor, Ejaz Ahmed, Benjamin Pick, Sharif Abuadbba, Jason, Xue, Raj Gaire, Shuo Wang, Seyit Camtepe, Surya Nepal

TL;DR
This paper analyzes malicious URL campaigns from SIEM alerts, grouping millions of URLs into campaigns to reveal threat patterns, evasive tactics, and gaps in detection, thereby enhancing threat intelligence for SOC teams.
Contribution
It introduces a campaign-based analysis of malicious URLs from large-scale data, uncovering thousands of campaigns and highlighting detection gaps without relying on machine learning.
Findings
Identified 77,800 malicious URL campaigns from 311 million records.
Only 2.97% of campaigns were detected by security vendors.
Revealed evasive tactics like longer URLs and diverse domains.
Abstract
The daily deluge of alerts is a sombre reality for Security Operations Centre (SOC) personnel worldwide. They are at the forefront of an organisation's cybersecurity infrastructure, and face the unenviable task of prioritising threats amongst a flood of abstruse alerts triggered by their Security Information and Event Management (SIEM) systems. URLs found within malicious communications form the bulk of such alerts, and pinpointing pertinent patterns within them allows teams to rapidly deescalate potential or extant threats. This need for vigilance has been traditionally filled with machine-learning based log analysis tools and anomaly detection concepts. To sidestep machine learning approaches, we instead propose to analyse suspicious URLs from SIEM alerts via the perspective of malicious URL campaigns. By first grouping URLs within 311M records gathered from VirusTotal into 2.6M…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Spam and Phishing Detection · Information and Cyber Security
