Virtual Honeypots for Resource-Efficient Network Vulnerability Detection
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Solution Overview
Problem
Existing honeypot systems require additional computing resources and pose security risks, while existing network attack detection methods are inefficient in identifying vulnerable resources and require significant computational resources.
Innovation Solution
Utilize pre-existing network resources as virtual honeypots, analyzing their activity patterns to identify attack patterns and vulnerabilities, and implement a vulnerability model to predict and mitigate risks without the need for additional resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real honeypots are deployed to detect attacks and study attack patterns, then security detection capability is improved, but additional computing resources (CPU, memory, network bandwidth) are consumed and security risks to third-party resources increase
Solution Approach 1:
The patent creates virtual copies of honeypots using virtualization technology. Instead of deploying physical honeypot systems that consume real computing resources, the invention generates virtual representations of vulnerable services and systems that can be monitored without requiring additional physical infrastructure. These virtual honeypots are instantiated as software-based simulations that replicate the behavior and vulnerability profiles of target systems.
Solution Approach 2:
The system leverages existing network resources and infrastructure to provide honeypot functionality. Rather than requiring dedicated external resources, the invention utilizes the organization's current computing environment, network architecture, and available services to deploy virtual honeypots. The existing infrastructure serves dual purposes: maintaining normal operations while simultaneously providing the deceptive environments needed for attack detection and analysis.
2Reliability
If real honeypots are deployed to attract and detect cyber attackers, then attack detection capability is improved, but security risks to third-party co-located resources increase
Solution Approach 1:
The patent segments the honeypot functionality from the production environment using virtualization. Virtual honeypots are isolated in separate virtualized containers or virtual machines that are logically separated from third-party resources and production systems. This segmentation ensures that any attacks directed at the virtual honeypots remain contained within the virtualized environment and cannot compromise adjacent systems or services hosted on the same physical infrastructure.
Solution Approach 2:
The virtualization layer acts as an intermediary between the honeypot functionality and the underlying physical infrastructure. This intermediary layer provides isolation and protection, allowing virtual honeypots to be deployed on shared hardware without exposing third-party resources to attack risks. The virtualization middleware captures and contains malicious activities, preventing them from propagating to co-located systems while still allowing the honeypots to effectively attract and detect attackers.
3Reliability
If traditional network attack detection methods are used to identify vulnerable resources, then security monitoring is performed, but computational resources are significantly consumed and detection efficiency is low
Solution Approach 1:
The patent replaces traditional mechanical scanning and probing methods with virtualized deception techniques. Instead of actively scanning networks to detect vulnerabilities—which consumes significant computational resources and can trigger false positives—the system passively presents virtual honeypots that naturally attract attackers. Attack detection occurs organically through the interaction between attackers and the virtualized deceptive environments, eliminating the need for resource-intensive active scanning while improving detection efficiency.
Data Source
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AI summary
A system for predicting vulnerability of network resources is provided. The system can calculate an initial vulnerability score for each of the network resources and use the initial vulnerability scores along with activity data of the network resources to train a vulnerability model. After training, the vulnerability model can predict the vulnerability of the network resources based on new activity data collected from the network resources. Based on the predicted vulnerability, vulnerable network resources can be identified. Further analysis can be performed by comparing the activities of the vulnerable network resources and other network resources to identify activity patterns unique to the vulnerable network resources as attack patterns. Based on the attack patterns, one or more actions can be taken to increase the security of the vulnerable network resources to avoid further vulnerability.