Multi-Layered IoT Policy Management for Fewer False Positives
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Solution Overview
Problem
Existing systems struggle to effectively manage and enforce policies across multiple layers of abstraction for IoT devices, leading to inefficiencies and increased false positives in network security and management.
Innovation Solution
A multi-layered policy management system that includes context-level, packet-level, event-level, and activity-level policy management engines, utilizing machine learning to generate and enforce policies based on device personalities and behaviors, reducing reliance on low-level pattern matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-level pattern matching is used for policy enforcement, then implementation simplicity is maintained, but false positives increase and accuracy deteriorates
Solution Approach 1:
The system segments policy management into multiple hierarchical levels: domain-level policies (high abstraction) and packet-level policies (low abstraction). Each level handles different aspects of policy enforcement, with domain-level policies capturing behavioral patterns and packet-level policies handling specific traffic rules. This segmentation allows the system to achieve high accuracy through domain-level context while maintaining implementation simplicity at the packet level.
Solution Approach 2:
The patent introduces domain-level policies as an intermediary layer between high-level security requirements and low-level packet inspection. These domain-level policies act as mediators that translate abstract security goals into concrete enforcement rules, reducing false positives by understanding the context and intent behind traffic patterns rather than relying solely on low-level pattern matching.
2Reliability
If multi-layered policy management is implemented, then policy enforcement quality improves, but system complexity increases
Solution Approach 1:
The system divides policy management into distinct hierarchical layers (domain-level and packet-level), each with specific responsibilities. Domain-level policies handle high-level behavioral patterns and context, while packet-level policies enforce specific traffic rules. This segmentation improves reliability by ensuring comprehensive policy coverage while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent adds a hierarchical dimension to policy management by introducing domain-level abstraction above packet-level enforcement. This dimensional change allows the system to enforce policies at multiple levels of granularity simultaneously, improving reliability through layered validation while organizing complexity in a structured hierarchical framework.
3Measurement precision
If context-level understanding is incorporated, then false positives are reduced, but processing requirements and complexity increase
Solution Approach 1:
The system performs preliminary action by pre-defining domain-level policies that capture contextual understanding of device behaviors and network patterns. These domain-level policies are established beforehand to provide context for packet-level enforcement, reducing false positives by having contextual rules ready before packet inspection occurs, rather than analyzing context in real-time for each packet.
Data Source
AI summary
Techniques for enforcing policy on multiple levels are disclosed. A multi-level policy includes at least one policy at a low level of abstraction and at least one policy at a high level of abstraction. An Internet of Things (IoT) device is discovered on a network. The IoT device is classified. The set of multi-level policies is applied to the IoT device based on the classification of the IoT device.