Dynamic Timeout for Remote Security Services
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Solution Overview
Problem
Enterprise networks face challenges in securing endpoints that regularly interact with external content and services, as existing techniques are inadequate in managing security locally and effectively, especially in heterogeneous networks where device compatibility and security policy compliance are difficult to enforce.
Innovation Solution
A threat management facility that remotely stores global reputation information is combined with a locally deployed machine learning classifier on endpoints to evaluate the risk of network communications, dynamically setting timeouts for requests and adjusting security parameters, such as scanning aggressiveness and content retrieval rates, to enhance security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a remote threat management facility is used to store global reputation information, then security coverage is improved, but network latency and response time increase
Solution Approach 1:
The security system is segmented into two parts: a remote threat management facility for comprehensive global reputation data and a local machine learning classifier for rapid risk assessment. This segmentation allows the system to maintain both broad security coverage and fast response times by dividing functionality between centralized and distributed components.
Solution Approach 2:
The local machine learning classifier performs preliminary risk assessment of network communications before queries are sent to the remote threat management facility. This preliminary action filters out low-risk communications locally, reducing network latency for safe communications while maintaining comprehensive security coverage for suspicious activities.
2Loss of time
If a local machine learning classifier is deployed on endpoints, then response time is improved, but device complexity increases
Solution Approach 1:
The machine learning classifier acts as an intermediary layer between the endpoint and the remote threat management facility. It simplifies the endpoint's burden by handling local risk assessments and only communicating with the remote facility when necessary, thereby improving response time while managing device complexity through intelligent delegation.
Solution Approach 2:
The system dynamically adjusts security parameters such as timeout values and scanning aggressiveness based on risk assessments. This allows the local classifier to operate efficiently with varying levels of complexity depending on the threat level, improving response time for low-risk communications while maintaining security for high-risk scenarios.
3Reliability
If dynamic timeout adjustment is implemented based on risk assessment, then security effectiveness is improved, but processing overhead increases
Solution Approach 1:
The timeout parameter is made dynamic rather than static, adjusting automatically based on the risk assessment of each network communication. High-risk communications receive longer timeouts for comprehensive checking, while low-risk communications use shorter timeouts, improving security effectiveness without uniformly increasing processing overhead across all communications.
Solution Approach 2:
Different timeout values and security parameters are applied locally to different network communications based on their specific risk profiles. This localized approach ensures that processing overhead is concentrated only on suspicious communications that require enhanced security checks, rather than applying uniform overhead to all network traffic.
4Reliability
If security parameters such as scanning aggressiveness are adjusted dynamically, then threat detection capability is improved, but system resource consumption increases
Solution Approach 1:
The system applies partial security measures (adjusted scanning aggressiveness) based on the assessed risk level of each communication. Low-risk communications receive minimal scanning to conserve resources, while high-risk communications receive more intensive scanning. This partial action approach maintains threat detection capability for suspicious activities while reducing system resource consumption for benign traffic.
Data Source
AI summary
A threat management facility that remotely stores global reputation information for network content can be used in combination with a recognition engine such as a machine learning classifier that is locally deployed on endpoints within an enterprise network. More specifically, the recognition engine can locally evaluate reputation for a network address being accessed by an endpoint, and this reputation information can be used to dynamically establish a timeout for a request from the endpoint to the threat management facility for corresponding global reputation information.