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Introduction: Two Techniques, One Story About Modern Threats
Cybersecurity rarely moves in a straight line. As defenders improve visibility and detection, attackers respond with creativity, subtlety, and technical improvisation. This article examines two seemingly different security research efforts that ultimately tell the same story: attackers continuously adapt to blind spots in defensive tooling, while defenders search for better ways to interpret complex data and expose hidden relationships.
The first part focuses on malware activity analysis using DShield sensor data, ELK, and graph-based visualization tools like Gephi and Graphviz. The second part examines a real-world phishing campaign that bypassed QR code detection by rendering codes using HTML tables instead of images. Together, these cases highlight how both sides of the security equation are evolving—one through data visualization and correlation, the other through evasion and abuse of assumptions.
Background: Why Visualization and Content Rendering Matter
Security telemetry grows faster than human intuition. Logs, hashes, IPs, and filenames quickly become overwhelming without tools that reveal structure. At the same time, email security solutions often rely on pattern recognition rooted in assumptions about how malicious content is normally delivered.
Both research efforts demonstrate what happens when those assumptions are challenged—either by visualizing data differently or by abusing overlooked technical details.
Summary of the Original Research and Findings
Data Collection from DShield Sensors
The first research effort centers on data collected by a DShield SSH honeypot sensor. Over a 30-day period, telemetry stored in an ELK stack was queried using Kibana’s ES|QL language. The objective was not simple counting, but relationship discovery—specifically how source IPs, malware filenames, file hashes, and sensors relate to one another.
To reduce noise, the dataset explicitly filtered out known researchers using the event.reference == “no match” tag added by Logstash. This ensured that the remaining data represented genuine malicious activity rather than controlled scanning or research traffic.
Query Design and Export Strategy
Two ES|QL queries were used. The first extracted source IPs, filenames, and sensor hostnames, while the second correlated source IPs with file hashes and filenames. Both queries limited results to 10,000 records, yielding 2,685 relevant samples over the observed period.
This structured export allowed the data to be imported into Gephi, where nodes and edges could be defined to represent IP addresses, file artifacts, and their interconnections.
Graph-Based Malware Clustering
Once visualized, the dataset revealed distinct clusters of activity. One group showed repeated uploads of files with identical filenames but different hashes, suggesting active development or polymorphic malware behavior. Another cluster was strongly associated with RedTail malware, linking multiple IPs, filenames, and hashes into a coherent attack pattern.
Gephi’s interactive capabilities allowed analysts to highlight specific nodes—such as a single IP address—and immediately see all related files and hashes fade into focus. This made it possible to visually track how a single actor reused infrastructure or malware artifacts over time.
Indicators of Compromise Identified
The research surfaced several actionable indicators, including multiple IP addresses and SHA-256 file hashes associated with malware delivery. These indicators can be operationalized for detection, blocking, or further investigation, illustrating how visualization can translate raw telemetry into defensive value.
Transition to QR Code–Based Phishing
The second half of the article shifts from network-level malware analysis to email-based phishing. Specifically, it documents a campaign observed between December 22 and December 26 that used QR codes to redirect victims to phishing sites.
While QR-based phishing—often called “quishing”—is not new, most modern email security products attempt to detect QR codes embedded as images. This campaign deliberately avoided images altogether.
HTML-Rendered QR Codes as an Evasion Technique
Instead of embedding an image, the attackers rendered QR codes using an HTML
<
table> composed of dozens of black and white cells. Visually, the QR code appeared legitimate and scannable, albeit slightly distorted. Technically, however, it bypassed QR detection engines that rely on image parsing.
Because the QR code existed only as structured HTML with background colors, many security tools treated it as benign layout code rather than encoded data.
Phishing Infrastructure and URL Structure
All QR codes pointed to subdomains of lidoustoo[.]click, with URLs dynamically personalized using recipient-specific values. Paths often included decimal or hexadecimal strings, along with the victim’s email address, suggesting automated campaign generation and tracking.
This personalization not only increased credibility but also complicated detection and takedown efforts.
Broader Security Implications
The campaign underscores a recurring issue in defensive security: many controls are built around expectations rather than guarantees. When attackers alter the form—but not the function—of malicious content, detection pipelines can fail silently.
The researchers conclude that while the technique itself is not novel, its real-world deployment is a reminder that technical defenses must constantly evolve and that user awareness remains a critical line of defense.
What Undercode Say: Why These Two Cases Matter Together
Visualization as a Force Multiplier
Graph-based analysis tools like Gephi fundamentally change how analysts reason about threats. Instead of examining logs line by line, defenders can observe behavior patterns, infrastructure reuse, and malware evolution at a glance. This approach is especially powerful when dealing with polymorphic malware, where traditional signature-based methods struggle.
The DShield analysis shows that visualization is not just cosmetic—it enables faster hypothesis testing and better intuition about adversary behavior.
The Danger of Implicit Assumptions
The QR phishing campaign exposes a common weakness in security engineering: implicit assumptions. Many detection systems assumed that QR codes would always be images. Once that assumption was broken, entire layers of defense were bypassed without exploiting a single software vulnerability.
This mirrors a broader trend in attacks that focus less on exploiting bugs and more on exploiting blind spots.
Data Context Beats Data Volume
Both cases reinforce the idea that more data does not equal better security. The ELK stack already contained the necessary telemetry, but without correlation and visualization, the deeper relationships remained hidden. Similarly, email gateways may log HTML content perfectly, but without contextual analysis, malicious intent goes unnoticed.
Security maturity depends on context, not quantity.
Attacker Innovation Is Often Low-Tech
Rendering a QR code using an HTML table is not technically sophisticated. Its effectiveness comes from understanding how defenses work and intentionally stepping just outside their detection boundaries. This aligns with many modern phishing and malware campaigns that prioritize reliability over complexity.
Defenders must therefore anticipate not just advanced exploits, but clever misuse of basic features.
The Human Factor Remains Central
Even the most advanced visualization tools and detection engines ultimately support human decision-making. In both studies, the final value emerged when analysts interpreted results, identified clusters, and questioned assumptions.
Likewise, user awareness remains critical in phishing defense. A QR code—no matter how cleverly rendered—still requires a human to scan it.
Fact Checker Results
✅ The ES|QL queries and Gephi-based analysis align with documented capabilities of ELK and graph visualization tools.
✅ HTML-rendered QR codes are a known but rarely deployed technique, accurately described in the campaign analysis.
❌ No evidence suggests this technique bypasses all email security products, only those relying solely on image-based QR detection.
Prediction: Where This Trend Is Headed 🔍📈
Defensive tooling will increasingly incorporate graph analytics and behavioral correlation as standard features, not optional enhancements. At the same time, phishing campaigns will continue to abandon obvious indicators like images and attachments in favor of structurally benign content that carries hidden intent. The next wave of attacks will not look more malicious—they will look more normal, forcing defenders to rethink how “safe” content is defined.
🕵️📝✔️Let’s dive deep and fact‑check.
References:
Reported By: isc.sans.edu
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