Persistent Digital Asset Detection via Periodic Scanning
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Solution Overview
Problem
Current external attack surface management solutions struggle to efficiently detect and manage persistent digital assets in large, complex networked computing environments, particularly in cloud services, leading to increased cybersecurity risks and operational costs.
Innovation Solution
A system configured to continuously detect digital asset information from a networked computing environment, update representations of digital assets, and generate new representations for newly detected assets, using techniques such as hash matching, attribute value analysis, and statistical distribution analysis to identify persistent digital assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated scanning solutions are used to detect digital assets, then detection coverage is improved, but scanning cost and time consumption increase
Solution Approach 1:
The system performs scanning at multiple discrete time points (first time, second time, third time) rather than continuous scanning, detecting digital assets at specific intervals. This periodic detection approach maintains detection coverage while significantly reducing overall scanning time and resource consumption compared to continuous monitoring.
Solution Approach 2:
The system performs preliminary detection at the first time point to identify and classify digital assets before they potentially become vulnerable. By preparing the asset inventory and risk assessment in advance, the system enables proactive security measures without requiring continuous real-time scanning, thus reducing overall time consumption.
2Measurement precision
If continuous scanning is performed to detect new devices, then detection accuracy is improved, but operational cost increases
Solution Approach 1:
The system transitions from continuous scanning to periodic scanning at three distinct time points. This approach maintains sufficient detection accuracy to identify new devices and updates while dramatically reducing operational costs associated with continuous monitoring infrastructure and resource consumption.
Solution Approach 2:
The system creates a static snapshot representation of the network asset state at each scanning time point rather than maintaining a continuously updated live model. This copying approach preserves detection accuracy for identifying changes between time points while reducing the computational and operational overhead of maintaining continuous scanning systems.
3Loss of information
If complete re-scanning is performed to ensure up-to-date asset profiles, then information freshness is improved, but time consumption increases
Solution Approach 1:
The system performs complete re-scanning at three periodic time points rather than continuously. This maintains information freshness by capturing asset states at multiple discrete moments, enabling the system to detect changes and updates while avoiding the time consumption of continuous scanning operations.
Solution Approach 2:
The system adapts its scanning strategy by performing complete re-scans at specific time points when changes are suspected or required, rather than maintaining a static continuous scanning approach. This dynamic scanning schedule ensures information freshness is maintained when needed while minimizing time consumption during stable periods.
Data Source
AI summary
A system and method for detecting persistent digital assets in an attack surface of a networked computing environment is disclosed. The method includes: continuously detecting digital asset information from a networked computing environment; updating a representation of a digital asset in response to determining that the digital asset information corresponds to a previously detected digital asset; and generating a representation of a digital asset in response to determining that the digital asset information corresponds to a new digital asset.