Context-Aware Threat Signature Selection for Network Traffic Scanning
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Solution Overview
Problem
Configuring security devices with an optimal number of signatures for scanning network traffic is challenging due to limited computing resources, leading to performance issues or missed threats.
Innovation Solution
A threat management device that determines signatures based on contextual data, such as operating system type, to selectively scan network traffic, allowing for efficient resource utilization and threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of signatures loaded into the security device increases, then the ability to detect threats is improved, but the computing resources needed to scan network traffic increase, negatively impacting device performance and network traffic flow
Solution Approach 1:
The patent applies local quality by customizing the signature set for each endpoint device based on its specific characteristics (operating system, applications, vulnerabilities). Instead of using a uniform comprehensive signature set for all devices, the system tailors the scanning signatures to match the actual threat profile of each endpoint, thereby reducing unnecessary scanning overhead while maintaining detection effectiveness for relevant threats.
Solution Approach 2:
The system dynamically changes the parameters of the signature set by selecting and loading only the relevant signatures needed for each endpoint device. This parameter optimization allows the security device to adjust its scanning behavior based on the specific vulnerability profile of each endpoint, reducing the overall number of signatures actively used and thereby decreasing computational resource consumption while maintaining adequate threat detection coverage.
2Reliability
If the number of signatures loaded into the security device increases, then more threats can be detected, but the computing resources required increase, leading to performance degradation
Solution Approach 1:
The patent extracts only the necessary signatures from the complete signature database and loads them into the security device for scanning. By identifying the specific vulnerabilities of each endpoint device and selecting only the signatures relevant to those vulnerabilities, the system removes unnecessary signatures from active use, thereby reducing the computational power required for scanning while maintaining adequate threat detection coverage.
Solution Approach 2:
The system applies partial action by using a subset of the total available signatures rather than all signatures. By determining the specific vulnerability profile of each endpoint and loading only the corresponding signatures needed for that endpoint, the system performs partial scanning with reduced resource consumption while still achieving effective threat detection for the specific device context.
3Reliability
If the security device is configured with an excessive number of signatures, then threat detection coverage is improved, but device performance and network traffic flow are negatively impacted
Solution Approach 1:
The system applies self-service by automatically determining the appropriate signature set for each endpoint device based on its vulnerability profile. The security device autonomously selects which signatures to load without requiring manual configuration by administrators, thereby reducing the complexity of signature management while ensuring that the correct signatures are applied for optimal threat detection coverage.
4Reliability
If the security device scans network traffic with all available signatures, then comprehensive threat detection is achieved, but resource utilization becomes inefficient
Solution Approach 1:
The patent implements local quality by customizing the signature scanning process for each endpoint device based on its specific vulnerability characteristics. By matching signatures to the actual vulnerabilities present on each device rather than applying all signatures universally, the system improves computing resource utilization efficiency while maintaining accurate threat detection for the relevant threat landscape of each endpoint.
Data Source
AI summary
Threat management devices and methods. The methods include receiving, at an interface of a threat management device, contextual data associated with a first endpoint device that is in operable connectivity with the threat management device, wherein the threat management device is configured to execute at least one subsystem to scan network traffic. The methods further include determining at least a first signature from a plurality of signatures to use in scanning the network traffic based on the received contextual data and instructing the at least one subsystem to scan network traffic using at least the first determined signature.