Detecting DNS Tunneling Activity in Grafana Server Environments Using Threat Intelligence and DNS Traffic Analysis

Organizations commonly use Grafana as a self-hosted observability and visualization platform for monitoring infrastructure, applications, and operational metrics. Because Grafana servers may have access to internal monitoring environments and sensitive operational information, attackers who compromise such systems may attempt to establish covert communication with external infrastructure.

One technique that can be used for covert communication is DNS tunneling. DNS tunneling abuses DNS queries and responses to transfer data or maintain communication through DNS traffic. Since DNS is commonly permitted in enterprise networks, this activity may blend into normal DNS communication.

Traditional network monitoring may identify DNS requests but may not provide enough context to determine whether the DNS traffic is associated with malicious infrastructure. Cyber Threat Intelligence can improve detection by identifying known malicious domains, IP addresses, and infrastructure associated with DNS tunneling, enriching these indicators with external intelligence, and correlating them with DNS activity observed around the Grafana environment.

In this use case, a self-hosted Grafana server is deployed on Ubuntu inside an isolated laboratory. A controlled DNS tunneling scenario is safely reproduced using dnscat2 specifically to demonstrate communication over DNS.

Yeti is used as the central CTI platform for collecting, organizing, and correlating threat indicators associated with suspicious DNS infrastructure. ThreatFox is used as an open-source threat-intelligence source for obtaining and enriching malicious infrastructure indicators.

Suricata monitors DNS and network traffic generated within the controlled environment, while OpenSearch is used for centralized investigation and visualization. The controlled DNS tunneling activity is correlated against threat intelligence to determine whether suspicious DNS infrastructure associated with the Grafana environment can be identified.

Grafana is the target application in this use case. It is deployed as a self-hosted monitoring and visualization platform inside the isolated laboratory. Grafana may provide access to infrastructure monitoring dashboards, application metrics, system performance information, operational information, monitoring data, and internal service information. If a Grafana server or its surrounding host becomes compromised, an attacker may attempt to communicate with external infrastructure through DNS.

DNS is an essential enterprise service and is normally allowed throughout internal environments. Because of this, malicious DNS communication can potentially blend with legitimate DNS activity.

Traditional network monitoring may identify DNS requests but may not provide enough context to determine whether observed DNS traffic is associated with suspicious or malicious infrastructure. Without threat intelligence correlation, potentially malicious communication may remain undetected.

The security problem is therefore:

The proposed solution introduces DNS threat intelligence collection, IOC validation, IOC enrichment, controlled DNS tunneling simulation, DNS traffic monitoring, threat intelligence correlation, suspicious-domain identification, threat-context analysis, risk-based investigation, and intelligence feedback.

The controlled attack scenario demonstrates how DNS can be abused as a communication channel. The simulation is performed only inside an isolated laboratory. dnscat2 is used to reproduce controlled DNS-based communication between authorized laboratory systems.

The detection focuses on identifying indicators such as repeated DNS queries, unusual query frequency, suspicious DNS domains, abnormal DNS patterns, high-volume DNS requests, long or unusual query names, known malicious infrastructure, and repeated communication with the same DNS infrastructure.

The primary security concept is Threat Intelligence-Driven DNS Monitoring.

The objective is to combine DNS network telemetry with threat intelligence to identify infrastructure potentially associated with DNS tunneling. The assessment considers DNS query frequency, DNS query patterns, query length, destination domain, destination IP, domain reputation, IOC confidence, threat intelligence source, repeated DNS communication, associated infrastructure, and potential command-and-control characteristics.

The secure processing flow is: