Mapping Cyber Bot Behaviors: Understanding Payload Patterns in Honeypot Traffic.

Area of Science:

• Cybersecurity
• Network Security
• Machine Learning

Background:

• Cyber bots are prevalent, necessitating behavioral understanding for effective threat intelligence.
• Analyzing bot behaviors is challenging due to blended traffic payloads and evolving techniques.
• Honeypot sensors are crucial for capturing and analyzing bot traffic, but existing methods struggle with scale and diversity.

Purpose of the Study:

• To map cyber bot behaviors through payload pattern analysis in honeypot traffic.
• To develop an adaptable framework for analyzing diverse and large-scale bot payloads.
• To extract actionable threat intelligence from botnet communication patterns.

Main Methods:

• An 11-month measurement study of cyber bot behaviors using honeypot traffic.
• Development of TrafficPrint, a pattern extraction framework combining representation learning and clustering.
• Automatic extraction of human-understandable payload patterns without protocol-specific expertise.

Main Results:

• Collected 21.5 million application-layer payloads from globally distributed honeypot sensors.
• TrafficPrint extracted 296 patterns, automatically labeling 83.57% of unknown payloads from a small labeled dataset.
• Identified that 82% of bot patterns use semi-customized structures, 13% contain attribution markers, and bots employ evasion techniques.

Conclusions:

• TrafficPrint provides an adaptable and effective method for analyzing diverse honeypot payloads.
• Payload pattern analysis yields actionable threat intelligence, including bot customization, attribution markers, and evasion strategies.
• The findings enhance understanding of cyber bot ecosystems and inform cybersecurity defenses.