Cloud Deception Scoring for High-Fidelity Threat Detection
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Solution Overview
Problem
Conventional deception technology solutions require on-premises appliances, which do not scale and necessitate infrastructure in the customer network, and security teams struggle to separate signal from noise in detecting advanced cyber threats.
Innovation Solution
Integration of deception technology with a cloud-based security system that deploys breadcrumbs/honeypots without on-premises appliances, enabling dynamic risk scoring and targeted threat detection based on user type, with all hits on breadcrumbs/honeypots being malicious by definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deception technology with on-premises appliances is used, then threat detection capability is provided, but device complexity and infrastructure requirements increase
Solution Approach 1:
The patent extracts the deception technology functionality from on-premises appliances and relocates it to a cloud-based platform. The system removes the need for customers to maintain physical or virtual appliances by moving all deception infrastructure to the cloud, thereby reducing device complexity while preserving threat detection capabilities through remote deployment and management of honeypots and decoys
Solution Approach 2:
The patent introduces a cloud-based intermediary platform that mediates between the customer's network and the deception infrastructure. This cloud intermediary handles the deployment, management, and data collection for honeypots and decoys, eliminating the need for customers to directly manage complex on-premises deception technology while maintaining effective threat detection
2Reliability
If conventional deception technology is used, then security monitoring is provided, but scalability is limited
Solution Approach 1:
The patent transitions from a two-dimensional on-premises deployment model to a three-dimensional cloud-based model that leverages virtualization and remote infrastructure. This dimensional shift enables the system to scale dynamically across multiple cloud regions and environments, allowing security monitoring to expand without physical constraints while maintaining full functionality through virtual honeypots and decoys deployed in cloud environments
3Difficulty of detecting and measuring
If all network activity is monitored to detect threats, then detection coverage is improved, but signal-to-noise ratio deteriorates
Solution Approach 1:
The patent converts the potentially harmful effect of monitoring all network activity into a beneficial approach by using deception technology. Honeypots and decoys are deliberately placed in the network to attract and expose malicious activity. By defining what should never be accessed (decoy resources), the system creates a clear signal for malicious behavior, improving the signal-to-noise ratio while maintaining comprehensive detection coverage through the deception framework
Data Source
AI summary
Systems and methods are provided for evaluating the effectiveness of network security tools for mitigating network security risks. According to one implementation, a method includes the step of analyzing a network to measure security parameters associated with the use of one or more network security tools that are configured for mitigating risk with respect to network compromise, data loss, lateral movement, and asset exposure. Based on the measured security parameters, the method further includes the step of quantifying the one or more network security tools to determine an effectiveness score defining an ability of the one or more network security tools, in combination, to counteract the network compromise, data loss, lateral movement, and asset exposure.