Distributed Honeynet Architecture for Detailed Cyber Threat Intelligence
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Solution Overview
Problem
Existing honeypot systems are vulnerable to detection by experienced attackers due to centralized architectures emulating multiple services on a single machine, leading to a single point of failure and inadequate threat intelligence on attacker techniques.
Innovation Solution
Decouple honeypots into honeynets that emulate similar services, each with dedicated intrusion detection and deep packet inspection, allowing for distributed and scalable deployment on cloud platforms, capturing detailed forensic data logs for threat intelligence generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple services are emulated on a single honeypot machine, then device complexity is reduced and ease of operation is improved, but reliability deteriorates due to single point of failure and detectability increases
Solution Approach 1:
The patent segments the honeypot system into multiple independent honeypot instances distributed across multiple machines. Each honeypot emulates specific services independently, eliminating the single point of failure problem. When one honeypot is compromised or fails, others remain operational, thus improving reliability while maintaining ease of operation through centralized management of the segmented system.
2Device complexity
If multiple services are emulated on a single honeypot machine, then device complexity is reduced, but detectability worsens as attackers can identify honeypots by characteristics like multiple low-interaction honeypots on a single machine
Solution Approach 1:
The system segments honeypots across multiple machines, making each individual honeypot appear as a legitimate single-service system rather than a multi-service honeypot. This distribution pattern mimics normal network infrastructure, reducing detectability while the underlying segmented architecture maintains manageable complexity through automated orchestration.
Solution Approach 2:
Each honeypot instance is configured with local quality - emulating specific services appropriate to its designated machine and network position. This creates realistic variations in service distribution across the network, making the overall system harder to detect as a honeynet while maintaining operational simplicity through centralized configuration management.
3Ease of operation
If centralized honeypot architecture is used, then ease of operation is improved, but information completeness deteriorates due to inadequate threat intelligence on attacker techniques
Solution Approach 1:
The segmented honeypot architecture captures diverse attacker behaviors across multiple independent instances, each exposing different services and configurations. This segmentation enables collection of more complete threat intelligence data about various attack techniques, while centralized management systems aggregate and analyze this distributed information, maintaining ease of operation.
Solution Approach 2:
The system implements feedback loops where data from segmented honeypots is continuously collected, analyzed, and used to improve threat intelligence. This feedback mechanism ensures that information from multiple honeypot instances is aggregated to provide comprehensive insights into attacker techniques, overcoming the information completeness problem while maintaining operational simplicity.
Data Source
AI summary
The present disclosure provides a system for generating cyber threat intelligence. The system includes a plurality of honeynets configured to emulate one or more services; a plurality of sensors, each sensor associated with a honeynet, each sensor configured to detect cyberattacks on the associated honeynet; a data collector configured to receive data relating to the cyberattacks on the plurality of honeynets; and a computing device configured to detect, from the sensors, one or more cyberattacks on the honeynets based on analysis of network traffic through the honeynets; extract, from detected cyberattacks on the honeynets, a detailed forensic data log based on analysis of content of the data packets pertaining to the cyberattacks on the honeynets; and transmit the detailed forensic data log to the data collector. The data collector stores the detailed forensic data log for further analysis in order to generate cyber threat intelligence.