Hardware Neural Network for Real-Time Cyber Anomaly Detection
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Solution Overview
Problem
Current cyber security methods for detecting malicious data patterns are slow and inefficient, requiring a faster and more accurate process to identify and detect anomalies in real-time, particularly due to the large volume of data that needs to be processed.
Innovation Solution
A hardware-based single layer artificial neural network is used to learn and detect pattern changes in data buses, instruction buses, or data packets, leveraging the change in learning rate to differentiate between normal and malicious patterns, and to detect imminent system failures by monitoring system parameters like temperature, speed, and vibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If software-based detection techniques are used to sift through voluminous amounts of data, then detection accuracy can be maintained, but the processing speed becomes slow and painstaking
Solution Approach 1:
The patent replaces software-based detection systems with a hardware-based artificial neural network system. The neural network is implemented in hardware (ASIC, FPGA, or dedicated neural network processor) to perform parallel processing of data patterns, thereby achieving both high detection accuracy and fast processing speed simultaneously. This substitution of mechanical/software systems with hardware-based intelligent systems resolves the contradiction between accuracy and speed.
2Quantity of substance
If traditional software-based methods are used to process large amounts of data in real-time, then comprehensive analysis can be performed, but the process becomes slow and inefficient
Solution Approach 1:
The patent replaces traditional software-based data processing with a hardware-based artificial neural network that can process voluminous data in real-time. The hardware implementation enables parallel processing architectures that simultaneously analyze multiple data streams, achieving both high data processing volume and detection efficiency.
Solution Approach 2:
The patent employs dynamic learning rates in the neural network that automatically adjust based on the complexity and characteristics of the data being processed. This dynamic adaptation allows the system to optimize processing speed for different data volumes while maintaining detection accuracy, thereby resolving the contradiction between data quantity and processing efficiency.
3Measurement precision
If software-based detection is used to identify malicious patterns, then thorough analysis is possible, but the detection process becomes slow and painstaking
Solution Approach 1:
The patent replaces software-based pattern detection with hardware-based artificial neural networks that can perform comprehensive pattern analysis in parallel. This hardware implementation maintains thorough detection capabilities while reducing detection time from minutes or hours to milliseconds, effectively resolving the contradiction between detection accuracy and time consumption.
Data Source
AI summary
A hardware-based artificial neural network receives data patterns from a source. The hardware-based artificial neural network is trained using the data patterns such that it learns normal data patterns. A new data pattern is identified when the data pattern deviates from the normal data patterns. The hardware-based artificial neural network is then trained using the new data pattern such that the hardware-based artificial neural network learns the new data pattern by altering one or more synaptic weights associated with the new data pattern. The rate at which the hardware-based artificial neural network alters the one or more synaptic weights is monitored, wherein a training rate that is greater than a threshold indicates that the new data pattern is malicious.