Blockchain-Encrypted Schema for Enterprise Network Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern networks face increased complexity and vulnerabilities, with existing cybersecurity solutions struggling to effectively detect anomalies due to noise from large numbers of devices with varying operating systems and hardware, leading to low detection rates and high false positives, and traditional security approaches are inadequate for long-running communication environments like IoT and 5G.
Innovation Solution
A blockchain-encrypted schema is implemented to enhance network communications, with a virtual communications kernel that restricts external hardware component interactions unless authorized, and a distributed End-to-End AI system that uses machine learning for continuous threat detection and remediation, enabling powerful automation and adaptive security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security products use historical threat metadata and static detection methods, then they can identify known threats, but they fail to detect novel threats and generate high false positive rates due to noise from normal device behavior
Solution Approach 1:
The patent implements dynamic behavioral baselines that continuously adapt to normal device operations rather than using static historical threat metadata. The system learns and updates what constitutes normal behavior for each device over time, allowing it to detect anomalies that deviate from these dynamic baselines. This resolves the contradiction by making the detection system adaptive and reliable without requiring complex manual configuration of static rules.
Solution Approach 2:
The system performs self-learning and automatic baseline establishment without requiring external intervention or complex configuration. Each device automatically generates its own behavioral baseline through continuous monitoring and machine learning, enabling the system to serve itself in detecting threats while reducing operational complexity.
2Reliability
If security systems monitor all device communications and operations across large enterprise networks, then they can detect threats, but they generate excessive noise and false alerts due to the sheer volume of normal device behavior variations
Solution Approach 1:
The patent applies local quality by creating device-specific behavioral baselines rather than using uniform monitoring rules across all devices. Each device is monitored according to its own learned normal behavior patterns, allowing the system to detect anomalies locally at each device level while filtering out the noise of normal variations. This significantly improves the signal-to-noise ratio by making detection criteria tailored to each device's characteristics.
3Reliability
If existing security solutions are placed directly on devices to monitor communications and operations, then they can detect threats at the source, but they increase device operational loads and complexity
Solution Approach 1:
The system enables devices to perform self-monitoring and self-protection by automatically establishing behavioral baselines and detecting anomalies without requiring heavy external security software. The lightweight agent on each device leverages the device's own operational data to detect threats, reducing the need for complex external monitoring infrastructure and lowering operational loads.
4Reliability
If machine learning algorithms are applied to filter anomalies through noise in large device networks, then they can improve detection, but they achieve limited effective detection rates and suffer from high false alert rates
Solution Approach 1:
The patent improves measurement precision by applying machine learning at the local device level rather than attempting to analyze all network traffic centrally. Each device's machine learning model is trained on its own behavioral data, creating highly specific detection criteria that accurately distinguish between normal variations and actual threats. This local approach dramatically reduces false positives while maintaining high detection rates.
Data Source
AI summary
A system and method for communicating over a network, including encrypting and decrypting communications of data over the network for providing enhanced security utilizing a blockchain-encryption process and a global device ledger, and further including systems for device and session initialization, automation, data capture, security, providing alerts, personalization of settings, and other objectives. Methods of establishing and monitoring network communications are further included.