Telecom Decoy Nodes for Real-Time Cyber-Threat Disruption
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Solution Overview
Problem
Existing telecommunications networks lack effective methods to identify and disrupt cyber-threats, particularly from malicious entities and bots, which exploit vulnerabilities within the network.
Innovation Solution
Deployment of decoy nodes that mimic actual network nodes to deceive attackers, combined with a monitoring system that analyzes interactions using machine learning to detect patterns indicative of malicious activity, followed by real-time classification and notification or service disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network monitoring methods are used, then network operations are maintained, but cyber-threats from malicious entities and bots cannot be effectively identified or disrupted
Solution Approach 1:
The patent deploys decoy nodes that are copies of legitimate network nodes, designed to mimic their behavior and appearance. These decoy nodes attract and trap malicious entities and bots, allowing security monitoring without requiring complex analysis of actual network traffic. The copying principle enables threat identification by creating artificial targets that replicate the characteristics of real nodes.
Solution Approach 2:
The patent introduces decoy nodes as intermediary elements between legitimate network operations and security monitoring. These intermediaries absorb malicious activity and provide a buffer that simplifies threat detection. The decoy nodes mediate between the network infrastructure and security analysis, reducing the complexity of direct monitoring.
2Object-affected harmful factors
If decoy nodes are deployed to mimic actual network nodes, then malicious entities and bots can be deceived and disrupted, but the complexity of the network infrastructure increases
Solution Approach 1:
Decoy nodes are created as simplified copies of legitimate network nodes, replicating only the essential characteristics needed to attract and trap malicious entities. This copying approach allows disruption of harmful factors without requiring full replication of complex network infrastructure, thereby limiting the increase in overall system complexity.
Solution Approach 2:
The decoy nodes serve multiple functions: they mimic legitimate nodes to attract attackers, provide safe targets for malicious activity, enable monitoring of threat patterns, and can be deployed in various network configurations. This multi-functionality reduces the need for separate specialized components, thereby managing infrastructure complexity while achieving comprehensive threat disruption.
3Measurement precision
If machine learning analysis is used to detect patterns of malicious activity, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The decoy nodes are pre-configured with known vulnerability patterns and behavioral characteristics that match common attack vectors. This preliminary setup allows malicious entities to self-identify through their interaction patterns with the decoys, reducing the need for extensive real-time analysis. The machine learning system only needs to analyze pre-defined patterns rather than evaluating all possible behaviors from scratch.
Solution Approach 2:
The patent extracts and isolates specific behavioral patterns that indicate malicious activity from the complex data generated by network interactions. By focusing machine learning analysis only on these extracted key patterns rather than the entire data set, the system achieves high detection accuracy while minimizing processing time and computational resource requirements.
Data Source
AI summary
Aspects herein provide systems, devices, methods, and media for capturing, monitoring and thwarting malicious traffic within a telecommunication network. In aspects, a plurality of decoy nodes are deployed throughout the telecommunications network, wherein the decoy nodes provide realistic outputs and interfaces in response to input and user interactions. Interactions may be intelligently classified. Depending on the classifications, notifications may be communicated and/or action may be initiated the impeded malicious traffic and/or cyber threat actors in near real-time with the interactions.