Cybersecurity Change Detection With LSTM-Guided Remediation

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Solution Overview

Problem

Existing cybersecurity solutions rely on manual detection and mitigation of unauthorized changes, which are time-consuming and prone to human-induced errors, and lack timely detection of vulnerabilities or potential exploits.

Innovation Solution

A cybersecurity solution utilizing Long Short-Term Memory (LSTM) analysis and heatmap representation to automate the detection of changes in a network environment, determining the frequency, affected parameters, and severity of changes, and generating remediation commands to adjust configuration settings.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual detection and mitigation methods are used, then human operators can identify and respond to unauthorized changes, but the process is time-consuming and prone to human errors

Engineering Contradiction:

Improvedetection accuracyVSAvoidresponse time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-diagnosis and self-response through automated LSTM analysis of network traffic patterns. The machine learning model independently identifies unauthorized changes and triggers remediation actions without human intervention, eliminating human error and reducing response time while maintaining high detection accuracy through continuous pattern recognition

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical detection processes with automated computational analysis using LSTM neural networks. The system substitutes human operators with machine learning algorithms that analyze network traffic patterns, detect anomalies, and execute remediation commands automatically, achieving faster and more reliable detection without human-induced errors

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated LSTM analysis and heatmap representation are implemented, then detection speed and accuracy improve, but system complexity increases

Engineering Contradiction:

Improvedetection speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces heatmap representation as an intermediary visualization layer between the complex LSTM analysis engine and the user interface. The heatmap translates complex multidimensional network traffic patterns into intuitive visual displays, allowing operators to understand automated detection results without needing to comprehend the underlying complex machine learning algorithms, thus maintaining high detection speed while reducing perceived system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260064837A1Countermeasure reactionary response related to changes in the it environment

Publication Date: 2026.03.05 SAUDI ARABIAN OIL CO

AI summary

A method and a system for intrusion detection and countermeasure reactionary response. The method may include obtaining, by the computer processor, a baseline of a cybersecurity environment and detecting a change in the cybersecurity environment. Further, the method includes performing a Long Short Term Memory (LSTM) analysis of the change to determine a frequency of the change, a plurality of affected parameters, and a nature of the change and determining a severity of the change based on a heatmap analysis of the frequency of the change and the LSTM analysis. A remediation command is generated based on the LSTM analysis and the severity of the change and the remediation command configured to adjust at least one configuration setting of a network is transmitted.