Dynamic Honeypot Conversion in Networked Computing
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Solution Overview
Problem
Traditional automated honeypot generation in cloud environments is limited in effectively distinguishing between valid and compromised systems, as legitimate users are often routed to the valid environment, allowing sophisticated attackers to identify decoy environments easily, and systems that create honeypots in response to attacks can be compromised by monitoring user flows.
Innovation Solution
A method and system that detect breaches in a networked computing environment by determining a risk factor based on proximity to the breached system, converting at-risk systems to decoy systems at their current location and re-generating them at a new location with a lower risk factor, while maintaining valid systems unchanged and routing users to the new system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If honeypots are created upfront in multiple application environments, then legitimate users can be protected by routing them to valid environments, but sophisticated attackers can easily identify valid environments and the value of decoy environments is limited
Solution Approach 1:
The system dynamically converts systems to honeypots based on real-time risk assessment after breach detection, rather than having static pre-defined honeypots. This dynamic adaptation prevents attackers from easily identifying decoy environments while maintaining reliable user routing to valid systems.
Solution Approach 2:
The system changes the state parameter of affected systems from 'valid' to 'honeypot' based on proximity-based risk factors. This parameter change allows the system to adapt its security posture dynamically, making it difficult for attackers to identify which systems are genuine while preserving user access to legitimate services.
2Reliability
If honeypots are created in response to attacks by migrating valid systems away from exposed systems, then valid users need to be migrated to new systems, but attackers can monitor user flow to recognize compromised environments
Solution Approach 1:
The system extracts the harmful information (user flow patterns) from the migration process by converting affected systems to honeypots instead of simply migrating users. This extraction prevents attackers from using flow monitoring to identify compromised environments while maintaining security integrity.
Solution Approach 2:
The system converts the potentially harmful information leakage from user flow monitoring into a benefit by transforming affected systems into honeypots. This conversion uses the situation where user flow might be monitored as an opportunity to create decoy environments that appear to be valid systems, thereby protecting the actual valid systems while maintaining service continuity.
3Reliability
If all systems are converted to honeypots after a breach, then security is enhanced, but network resources are wasted converting low-risk systems
Solution Approach 1:
The system applies different quality treatments to different systems based on their individual risk profiles. Systems with high proximity-based risk factors are converted to honeypots, while low-risk systems maintain their normal functionality. This local differentiation enhances security where needed while conserving network resources in low-risk areas.
Solution Approach 2:
The system performs partial conversion to honeypots only for systems that exceed a threshold risk level, rather than converting all systems. This partial action approach provides sufficient security coverage for high-risk systems while avoiding the excessive resource consumption that would result from converting the entire environment.
Data Source
AI summary
Approaches for providing security in a networked computing environment are provided. The method includes detecting, by at least one computer device, a breach of a first system in the networked computing environment. The method also includes identifying a second system in the in the networked computing environment as an at-risk system based on a proximity of the second system to the first system. The method additionally includes re-generating, by the at least one computer device, the second system as a new system at a new location in the networked computing environment. The method further includes converting, by the at least one computer device, the second system to a decoy system.