Cowrie Honeypot Analysis: Detecting Automated Session Disconnects

Honeypots serve as vital sensors for the cybersecurity community, providing early warnings of new TTP sets and widespread scanning activity. Among these, the Cowrie honeypot is widely deployed for capturing interaction data involving SSH and Telnet protocols. Recent research published by the SANS Internet Storm Center examines specific session behaviors within DShield honeypot instances to determine how attackers—and their automated scripts—interact with these environments before disconnecting.

Understanding the lifecycle of a session is essential for distinguishing between low-interaction scanning and more sophisticated manual activity. By performing Cowrie SSH honeypot session analysis, researchers can identify whether an attacker is simply running a hardcoded list of commands or if they are actively exploring the system.

Analyzing Command Volume and Session Duration

A significant portion of traffic captured by DShield sensors consists of repeated bot interactions. Many sessions result in immediate disconnection or involve no command execution after a successful login. This behavior typically indicates automated scanners looking for specific CVE vulnerabilities or brute-forcing credentials to build a botnet for DDoS attacks.

According to the source data, a large volume of sessions exhibit zero command execution. For sessions that do involve interaction, the command count often remains low, suggesting that most actors are utilizing scripts to perform initial reconnaissance or drop a payload. Analyzing the distribution of these commands allows analysts to identify sessions where the command count deviates from the norm, potentially signaling a human operator or a more complex APT script.

Identifying Automated Bot Traffic Patterns

One of the primary goals of analyzing session disconnects is identifying automated bot traffic patterns. Scripts typically follow a rigid logic: login, execute a specific series of commands (e.g., uname -a, cat /proc/cpuinfo, downloading a binary via wget), and then exit. When these scripts encounter an unexpected environment or fail to download a second-stage payload, they may terminate abruptly.

Security teams can use SIEM platforms to correlate session duration with command success rates. If a high volume of sessions from a specific IP address consistently lasts under five seconds and executes the same three commands, it is almost certainly a bot. Conversely, sessions that persist for several minutes and involve varied command syntax warrant closer inspection by the SOC as they may indicate manual Lateral Movement attempts.

How to Detect Honeypot Fingerprinting

A persistent challenge for threat researchers is identifying when actors use techniques to recognize they are in a sandbox. Sophisticated actors often run specific commands designed to reveal if they are operating within an emulated environment like Cowrie. If an actor detects inconsistencies in the file system or environment variables, they may disconnect immediately without running their primary payload to avoid exposing their IoCs.

The analysis of the last command executed before a session disconnects provides valuable insights. While many sessions end with a standard exit or logout command, others terminate due to timeouts or connection resets. A session that ends immediately after a command like cat /proc/mounts or ls -l /etc/passwd might suggest the attacker recognized the honeypot’s limitations.

Mitigation and Defensive Recommendations

While honeypot data is primarily for intelligence gathering, the patterns observed have direct applications for production security.

• Implement Session Monitoring: Use EDR or auditd to track the duration and command history of all SSH sessions, particularly those originating from external networks.
• Identify Anomalous Exits: Monitor for sessions that terminate immediately after reconnaissance commands, as this may indicate an attacker testing your environment’s defenses.
• Enforce Zero Trust Principles: Apply Zero Trust to administrative interfaces, ensuring that SSH access is restricted to known, authenticated users via multi-factor authentication, thereby neutralizing simple automated brute-force bots.

By focusing on these behavioral metrics, organizations can transition from reactive blocking to a more nuanced understanding of the threats targeting their infrastructure.