Dynamic Load Balancing via Image Classification for Cyber Attack Resistance
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Solution Overview
Problem
Computer networks are vulnerable to cyber-attacks that exploit specific weaknesses in load balancing techniques, allowing malicious users to effectively target and overwhelm the network by identifying the applied load balancing scheme.
Innovation Solution
A method and system that transform network metrics into digital images, using image recognition machine learning models to classify the network state and automatically switch to a different load balancing scheme to thwart cyber-attacks by continuously updating the load balancing technique based on the detected state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed load balancing scheme is applied to the computer network, then the network can maintain simple and stable operation, but the network becomes vulnerable to cyber-attacks that exploit specific weaknesses in the load balancing technique
Solution Approach 1:
The patent implements dynamic load balancing scheme selection where the system automatically switches between different load balancing techniques (e.g., round-robin, weighted round-robin, least connections) based on real-time network conditions and detected cyber-attack patterns. This dynamic adaptation prevents attackers from exploiting fixed scheme weaknesses while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system continuously monitors network metrics (traffic patterns, response times, resource utilization) and uses this feedback to classify network states and determine the optimal load balancing scheme. The feedback loop detects attack behaviors, classifies the network state, and automatically adjusts the load balancing technique to mitigate threats while maintaining security.
2Reliability
If the load balancing scheme is dynamically changed to resist cyber attacks, then network security improves, but the system complexity increases due to multiple load balancing schemes and classification mechanisms
Solution Approach 1:
The patent introduces an intermediary classification system that acts as a mediator between network monitoring and load balancing decision-making. The classification module processes network metrics and attack detection data, translating complex network states into standardized classifications that directly map to appropriate load balancing schemes. This intermediary layer simplifies the overall system architecture by providing a clear decision framework.
Solution Approach 2:
The system changes operational parameters by switching between different load balancing schemes based on detected network conditions. Instead of modifying the fundamental architecture, the system adjusts parameters such as weighting factors, selection criteria, and distribution algorithms to adapt to varying threat levels and network states, thereby reducing structural complexity while maintaining security.
3Adaptability or versatility
If multiple load balancing schemes are implemented to respond to different attack types, then the network can effectively counter diverse cyber threats, but the difficulty of detecting and measuring network state increases
Solution Approach 1:
The patent segments network monitoring into specific metric categories (traffic volume, response time, resource utilization, error rates) and classifies network states based on combinations of these segmented metrics. This segmentation makes detection and measurement more manageable by breaking down complex network states into discrete, measurable components that can be systematically evaluated for attack detection.
Solution Approach 2:
The system uses visual metaphors analogous to color changes to represent different network states and attack types through classification categories. Each network state or attack pattern is assigned a distinct classification label that can be easily distinguished and measured, similar to how different colors indicate different states. This categorization simplifies the detection and measurement of network conditions for automated response.
Data Source
AI summary
A method including transforming metrics, related to a computer network environment, into a digital image including pixels that represent the metrics. The computer network environment initially is load balanced by a first load balancing scheme selected from among load balancing schemes. The method also includes generating a classification of the digital image. The method also includes selecting, based on the classification of the digital image, a selected load balancing scheme from among the load balancing schemes. The method also includes changing the first load balancing scheme to the selected load balancing scheme such that the selected load balancing scheme is applied to the computer network environment.