Generative AI for IoT Cyber Threat Mitigation
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Solution Overview
Problem
Current cybersecurity measures for IoT environments face challenges due to device heterogeneity, resource constraints, fragmented regulations, large attack surfaces, supply chain security concerns, data privacy issues, integration with existing systems, and longevity and lifecycle management.
Innovation Solution
The use of generative artificial intelligence (AI) and machine learning (ML) algorithms to analyze and classify unstructured threat research reports, extract relevant information about indicators of compromise (IOCs), and generate network security recommendations tailored to specific IoT environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security solutions like firewalls, intrusion detection systems, and encryption algorithms are implemented in IoT environments, then security protection is improved, but resource consumption increases significantly
Solution Approach 1:
The patent extracts and separates security analysis functions from the resource-constrained IoT devices themselves, placing them on external servers. The system collects data from IoT devices and performs computationally intensive security analysis externally, allowing devices to maintain minimal local security functionality while benefiting from robust external protection without resource overhead.
Solution Approach 2:
The patent introduces an intermediary server system that acts as a mediator between IoT devices and security threats. This intermediary performs the heavy lifting of security analysis, pattern recognition, and threat mitigation, allowing resource-constrained devices to maintain security protection without directly consuming significant resources on local security operations.
2Measurement precision
If comprehensive security monitoring is implemented across large numbers of IoT devices, then threat detection capability is improved, but system complexity increases
Solution Approach 1:
The patent merges security monitoring functions across multiple IoT devices into a centralized system that processes data from numerous devices simultaneously. By consolidating analysis resources and using unified threat intelligence databases, the system achieves comprehensive threat detection across large device populations without each individual device or local system becoming complex.
Solution Approach 2:
The patent creates a universal security platform that serves multiple IoT devices across different environments and application types. The system performs multiple functions including threat detection, analysis, response coordination, and intelligence sharing, allowing a single system to handle diverse security requirements without proportionally increasing complexity for each device or application.
3Measurement precision
If detailed analysis of unstructured threat research reports is performed manually, then accuracy of threat classification is improved, but time consumption increases
Solution Approach 1:
The patent replaces manual mechanical analysis of threat reports with automated computational systems including natural language processing, machine learning models, and automated information extraction tools. These systems rapidly parse unstructured research reports, extract relevant threat intelligence, and classify threats with high accuracy without the time investment required for manual expert analysis.
Solution Approach 2:
The patent performs preliminary automated processing and pre-classification of threat research reports before human expert review. By using automated systems to initial triage, extract key information, and organize threat data in advance, the system reduces the time required for subsequent detailed analysis while maintaining or improving classification accuracy through consistent application of analysis criteria.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining a group of threat research reports, obtaining a group of indicators of compromise (IOCs), and monitoring a network of Internet of Things (IoT) devices. Further embodiments can include determining a group of possible malware on the network based on the monitoring of the network, and generating a network security recommendation based on the group of threat research reports based on the group of threat research reports, the group of IOCs, and the group of possible malware. Other embodiments are disclosed.