LSTM Analytics Engine for Network Data Corruption Detection
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Solution Overview
Problem
In computer networks, software or hardware faults in data routers can lead to significant adverse effects, such as incorrect data transmission, causing physical damage, technological failures, or financial losses, due to the inability to quickly identify and address node malfunctions.
Innovation Solution
A system comprising low-latency packet monitors and a data analytics engine is deployed between data routers and central repositories to detect corruption in data streams, using LSTM neural networks to determine the likelihood of corruption and transmit electronic messages to prevent further data corruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data routers are deployed to transmit and route data packets in computer networks, then network functionality and data transmission capability are improved, but the risk of software or hardware faults causing data corruption and significant adverse effects increases
Solution Approach 1:
The patent introduces an intermediary system consisting of packet capture nodes and an analytics engine that positions itself between the data router and the destination. This intermediary monitors data streams, detects anomalies using LSTM neural networks, and can shut down corrupted data transmission, thereby protecting data integrity without compromising the router's transmission functionality.
Solution Approach 2:
The system implements feedback by continuously monitoring data streams from routers, analyzing them for anomalies, and providing corrective actions when faults are detected. The analytics engine receives data streams, processes them through LSTM networks to detect corruption patterns, and triggers shutdown signals to prevent further corrupted data transmission, creating a closed-loop control system.
2Reliability
If traditional monitoring methods are used to detect router faults, then system complexity is reduced, but the detection speed and ability to prevent data corruption is insufficient
Solution Approach 1:
The patent replaces traditional mechanical or rule-based monitoring systems with an LSTM neural network-based analytics engine. This substitution enables the system to learn complex patterns of data corruption and detect faults with higher accuracy and speed, though it increases computational complexity.
Solution Approach 2:
The system performs preliminary action by continuously analyzing data streams in real-time and detecting anomalies before they cause significant damage. The LSTM network is trained to recognize patterns of corruption early, allowing the system to shut down corrupted transmissions before they propagate widely through the network.
3Loss of time
If packet capture nodes are deployed to monitor data streams in real-time, then detection speed and data integrity are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the monitoring function into distributed packet capture nodes that are positioned at strategic points in the network. Each node independently captures and forwards data streams to the analytics engine, distributing the monitoring workload and reducing the complexity burden on any single component while maintaining real-time detection capability.
Data Source
AI summary
A system for detecting a communications computer network node malfunction by analysis of network traffic output by the network node. Low latency packet capture nodes copy network traffic and transmit it to an analytics engine, which may use machine learning techniques, including long short-term memory (LSTM) neural networks, to determine a likelihood that the output of one data router in a network is suffering from a software malfunction, hardware malfunction, or network connectivity issue, and preserve overall data quality in the network by causing cessation of traffic by the malfunctioning node of the network.