Using SNI (Server Name Indication) and DNS Query Anomalies to Spot Automated Traffic

In the landscape of cybersecurity and web analytics, distinguishing between human visitors and automated traffic is crucial. Automated traffic, often generated by bots or malicious actors, can skew analytics, consume resources, or pose security threats. Traditional detection methods include analyzing user-agent strings or IP reputation, but these can be easily spoofed. A more sophisticated approach involves analyzing network-level signals, such as Server Name Indication (SNI) in TLS handshakes and DNS query patterns.

This blog explores how SNI and DNS query anomalies can be effective indicators of automated traffic, helping security teams and web administrators identify and mitigate unwanted activity.

Understanding SNI and DNS Query Basics

What is SNI?

Server Name Indication (SNI) is an extension to the TLS protocol that allows a client to specify the hostname it intends to connect to during the TLS handshake. Without SNI, a server hosting multiple domains on a single IP address cannot determine which certificate to present until after the handshake begins, which can cause issues with virtual hosting.

When a client initiates a TLS connection, it includes the hostname in the SNI field. This information helps servers deliver the correct SSL/TLS certificate, enabling secure connections for multiple domains sharing the same IP address.

What are DNS queries?

DNS queries are requests made by clients to resolve domain names into IP addresses. Monitoring DNS query patterns provides insights into user behavior, domain popularity, and potentially malicious activities. Anomalies in DNS queries—such as unusual query volumes, rare domain requests, or inconsistent patterns—can signal automated or malicious behavior.

How SNI and DNS Anomalies Indicate Automated Traffic

1. SNI Patterns and Anomalies

Automated traffic often exhibits characteristic patterns in SNI data:

Repeated or identical SNI fields: Bots might use the same hostname repeatedly, or cycle through a limited set of hostnames.
Use of non-standard or suspicious hostnames: Automated scripts may query domains that are malformed, contain unusual characters, or are known malicious domains.
Lack of SNI in TLS handshakes: Some automated tools or misconfigured clients may omit SNI entirely, which is uncommon in legitimate browser traffic.

2. DNS Query Anomalies

DNS query patterns can also reveal automation:

High query volume from a single source: Excessive DNS requests from a single IP or client suggest automation.
Querying uncommon or non-existent domains: Bots often probe non-standard domains or domains that do not exist.
Rapid or sequential querying: Automated scripts may perform rapid fire queries or sequential patterns that are unlikely from human users.
Mismatch between DNS queries and user activity: For example, DNS requests for domains unrelated to current browsing activity can be suspicious.

3. Combining SNI and DNS Data

By correlating SNI data with DNS query patterns, defenders can improve detection accuracy:

A client making TLS connections with suspicious or malformed SNI fields coupled with unusual DNS queries is likely automated.
Consistent anomalies across both protocols strengthen the suspicion of non-human traffic.

Practical Approaches to Detecting Automated Traffic

Monitoring and Logging

Implement comprehensive logging of TLS handshakes and DNS queries. Tools like network intrusion detection systems (IDS) and security information and event management (SIEM) platforms can aggregate these logs for analysis.

Anomaly Detection Techniques

Statistical analysis: Establish baseline patterns for SNI and DNS activity and flag deviations.
Signature-based detection: Maintain a list of known malicious hostnames and domains.
Behavioral analysis: Use machine learning models to identify patterns indicative of automation.

Real-time Detection

Integrate detection mechanisms into your network infrastructure to identify anomalies in real-time, enabling swift blocking or additional scrutiny of suspicious traffic.

Example Tools and Resources

Wireshark for packet analysis.
Bro/Zeek for network monitoring and scripting.
DNS logs from authoritative DNS servers.
Threat intelligence feeds to identify malicious domains.

Challenges and Limitations

While SNI and DNS anomaly detection is powerful, it is not foolproof:

Encrypted SNI (ESNI/ECH): Emerging protocols encrypt the SNI field, reducing visibility.
IP spoofing and VPNs: Sophisticated bots may use proxies or VPNs to obfuscate origin.
False positives: Legitimate users can sometimes trigger anomalies, requiring careful tuning.

Therefore, combining SNI and DNS analysis with other detection methods enhances overall effectiveness.

Conclusion

Leveraging SNI information and DNS query analysis provides a valuable layer of detection for automated traffic. By understanding typical patterns and identifying anomalies, security teams can better protect web resources from malicious bots, data scraping, and other forms of automated abuse. As protocols evolve and new privacy features emerge, staying informed and adapting detection strategies remains essential in maintaining secure and efficient online environments.

In summary, SNI and DNS query anomaly detection serve as effective tools in the ongoing effort to identify and mitigate automated traffic. Integrating these signals into broader security frameworks can significantly enhance your ability to distinguish between legitimate users and malicious actors.

Leveraging SNI and DNS Anomalies for Detecting Automated Traffic