Intelligent Feedback Loop for Network Traffic Filtering
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Solution Overview
Problem
Application-specific integrated circuits (ASICs) face challenges in adapting to changes in network topology and security protocols, leading to inefficiencies and increased risks of undetected cybercrime, as they struggle to process large volumes of data and require costly redesigns.
Innovation Solution
An intelligent feedback loop system within a network traffic monitoring system iteratively removes extraneous data from packets using content filtering algorithms, static filtering, and tuple hash algorithms, reducing processing power and memory usage through zero-copy drivers and buffers, and dynamically filters data to focus on actions of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If application-specific integrated circuits are used to process large volume network data, then data processing capability is improved, but adaptability to network topology changes deteriorates
Solution Approach 1:
The patent implements dynamic filtering where the ASIC continuously adapts its filtering rules based on feedback from the network traffic analysis system. The system dynamically updates packet classification rules, flow detection parameters, and filtering criteria to match changing network topology and security threats, allowing the hardware to remain effective without physical redesign.
Solution Approach 2:
The system employs a feedback mechanism where the network traffic analysis system monitors processed traffic patterns, identifies new threats or topology changes, and sends updated filtering rules back to the ASIC. This closed-loop feedback enables the ASIC to adapt its data processing behavior in real-time based on actual network conditions and emerging security requirements.
2Adaptability or versatility
If application-specific integrated circuits are redesigned to adapt to network changes, then adaptability is improved, but engineering cost and time increase
Solution Approach 1:
Instead of redesigning the entire ASIC for every network change, the system implements partial updates by only modifying the filtering rules and classification parameters that need adaptation. The core ASIC architecture remains fixed while only the configurable filtering layers are updated, significantly reducing engineering overhead.
Solution Approach 2:
The patent uses software-based filtering rule sets that can be copied and deployed to the ASIC without physical hardware changes. These digital rule configurations can be rapidly replicated and updated, replacing the need for expensive and time-consuming hardware redesign cycles.
3Measurement precision
If content filtering algorithms are applied to target data, then detection precision is improved, but processing time increases
Solution Approach 1:
The filtering process is segmented into multiple stages: initial packet classification filters obvious non-matching traffic first, then progressively more detailed content filtering is applied only to suspicious packets. This hierarchical segmentation ensures high detection precision for threats while minimizing processing time for legitimate traffic by applying intensive filtering only where necessary.
Solution Approach 2:
The system performs preliminary packet classification and basic filtering before applying detailed content filtering algorithms. By pre-processing and eliminating clearly non-matching packets early in the pipeline, the system prepares data in advance for more intensive analysis only when needed, reducing overall processing time while maintaining detection precision.
Data Source
AI summary
A method, apparatus and system related to an intelligent feedback loop to iteratively reduce target packet analysis is disclosed. According to one embodiment, a method of a network traffic monitoring system includes processing a flow data received through an aggregation switch of a network traffic monitoring system in a first stage module of the network traffic monitoring system, filtering the flow data to a target data based on a packet classification in the first stage module, determining that a portion of a target data is an extraneous data based on a content filtering algorithm applied in a data processing system of the network traffic monitoring system, and iteratively removing from the target data the extraneous data based on a feedback loop created between the data processing system and the first stage module of the network traffic monitoring system.