Network Anomaly Detection With Auto-Generated Policy Enforcement
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Solution Overview
Problem
Current network operating systems lack effective real-time protection against advanced persistent threats and day zero attacks due to increased data network speeds and complexity from vertical and horizontal network disaggregation, with existing methods failing to provide preemptive security against vulnerabilities in closed-source third-party software components and open-source components.
Innovation Solution
A system comprising a traffic collector, machine learning component, and policy component for real-time analysis of packet metadata to detect anomalies and autogenerate mitigation policies, which are enforced across network endpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard protocols (netflow, sflow) are used for telemetry data collection, then network monitoring is implemented, but real-time anomaly detection capability is insufficient
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing packet metadata in real-time to establish baseline behavioral models of network traffic before attacks occur. This enables the system to detect anomalies and generate mitigation policies proactively, rather than reacting after threats are identified through traditional offline analysis of netflow/sflow data.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network traffic, detecting anomalies, generating mitigation policies, enforcing those policies, and then monitoring the effects of enforcement to refine future detections. This closed-loop feedback enables real-time adaptation and improves security response capability beyond static protocol-based monitoring.
2Reliability
If dedicated network intrusion prevention systems are deployed, then statistical analysis of packet metadata is performed, but protection against day zero and advanced persistent threats is inadequate
Solution Approach 1:
The system achieves multi-functionality by integrating multiple capabilities into a unified platform: packet metadata collection, behavioral baseline establishment, anomaly detection, automated policy generation, and policy enforcement. This universal system replaces the need for separate dedicated intrusion prevention systems while providing broader protection against day zero and advanced persistent threats through machine learning-based anomaly detection.
Solution Approach 2:
The system performs self-service by automatically generating mitigation policies based on detected anomalies without requiring manual configuration or human intervention. The automated policy generation and enforcement capabilities enable the system to protect against advanced threats independently, reducing the complexity associated with manually managed intrusion prevention systems.
3Adaptability or versatility
If network disaggregation is implemented, then system flexibility is improved, but vulnerability to attacks increases due to multiple subcomponents
Solution Approach 1:
The system acts as an intermediary by deploying lightweight agents at network endpoints that collect packet metadata and enforce mitigation policies locally. This intermediary approach protects disaggregated network subcomponents by providing a unified security layer that monitors and controls traffic across multiple distributed components without requiring changes to the underlying disaggregated architecture.
Solution Approach 2:
The system applies local quality by enabling each network endpoint with a policy agent to independently enforce security policies specific to its local context. This distributed approach allows each subcomponent to have tailored security measures while maintaining overall system flexibility, reducing the attack surface by securing individual components rather than requiring centralized protection.
4Measurement precision
If real-time analysis of packet metadata is performed, then anomaly detection capability is improved, but processing complexity increases
Solution Approach 1:
The system extracts only the essential packet metadata fields required for anomaly detection rather than analyzing complete packet contents. By selectively extracting relevant metadata (such as source/destination addresses, ports, protocols, and timing information), the system achieves high anomaly detection accuracy while minimizing processing complexity and computational overhead.
Solution Approach 2:
The system applies partial action by focusing analysis on specific packet metadata characteristics that are most indicative of anomalies rather than examining all possible packet attributes. This selective analysis approach maintains high detection precision for security-relevant anomalies while avoiding the excessive complexity of comprehensive packet inspection.
Data Source
AI summary
This disclosure describes methods, devices, and systems for network anomaly detection and policy enforcement. An example method includes obtaining metadata for a plurality of network packets. The method also includes detecting an anomaly in the plurality of network packets by analyzing the obtained metadata and the operating information. The method also includes generating, without user input, a policy rule based on the detected anomaly. The method further includes enforcing the policy rule at the one or more network devices.