Endpoint Cybersecurity Detection via Local Event-Behavior Vector Comparison
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Solution Overview
Problem
Existing cybersecurity solutions require constant Internet access and are inefficient in detecting and responding to cybersecurity attacks on endpoint devices, leading to potential data breaches and system vulnerabilities.
Innovation Solution
A cybersecurity detection agent that generates and compares event behavior feature vectors locally with pre-defined cybersecurity attack feature vectors, allowing for real-time detection and response without constant network connectivity, using similarity analysis to identify and mitigate threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a cybersecurity detection agent performs local vector comparison, then detection speed and autonomy are improved, but the complexity of feature extraction and comparison algorithms increases
Solution Approach 1:
The system pre-computes cybersecurity attack feature vectors in the cloud before deployment to endpoint devices. These pre-computed vectors are stored locally and ready for immediate comparison, eliminating the need for real-time cloud computation and enabling fast local detection while reducing the computational burden on endpoint devices
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation that bridges cloud-based threat intelligence and local detection operations. Instead of directly comparing raw event data with cloud databases, the system uses compressed feature vectors as intermediaries, enabling efficient local comparison while maintaining connection to cloud-based threat intelligence
2Reliability
If the cybersecurity solution operates independently without constant Internet access, then reliability and autonomy are improved, but the ability to update threat signatures and receive cloud-based intelligence is reduced
Solution Approach 1:
The system performs preliminary actions by downloading and storing cybersecurity attack feature vectors in the cloud before deployment to endpoint devices. These pre-downloaded vectors enable the detection agent to operate autonomously offline while maintaining the ability to update when connectivity is available
Solution Approach 2:
The patent implements local quality by enabling the detection agent to function with full detection capabilities stored locally on the endpoint device. The system maintains high-quality autonomous operation with the ability to supplement with cloud-based updates when connectivity is available, creating a hybrid architecture that adapts to network conditions
3Ease of manufacture
If the detection agent remains lightweight and simple, then ease of deployment and resource consumption are improved, but the precision and accuracy of attack detection may be reduced
Solution Approach 1:
The system extracts only the essential features of cybersecurity attacks into compact feature vectors, separating the critical detection information from the full complexity of raw attack data. This extraction enables lightweight local agents to achieve high detection accuracy by focusing on the most discriminative features while maintaining simplicity
Solution Approach 2:
The patent transforms complex cybersecurity attack patterns into simplified feature vector representations with specific parameters. This parameter transformation maintains detection accuracy by preserving the essential characteristics of attacks in a compact format that is efficient for local comparison and processing
Data Source
AI summary
A cybersecurity service protects endpoint devices from cybersecurity attacks. The cybersecurity service deploys cybersecurity attack feature vectors to agents in the field. The cybersecurity attack feature vectors are created in the cloud to efficiently describe observed groups of cybersecurity attacks. One method to assemble these is to generate clustering centroids for the observed groups. Each agent monitors its host according to the cybersecurity attack feature vectors. Each agent monitors its host's event behaviors and locally extracts an event behavior feature vector. The agent compares the cybersecurity attack feature vectors to the event behavior feature vector and, if similarity is determined, then the agent determines that the host's event behaviors are evidence of a cybersecurity attack. The agent may implement threat procedures, such as suspending/terminating the event behaviors and generating alerts. The agent remains a small, lightweight cybersecurity detector that does not need constant Internet access.