Client-Hosted Neural Networks for Adaptive Behavior Trust Scoring
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Solution Overview
Problem
Current approaches for determining user trustworthiness in network-based computing services focus on monitoring client behavior by a service provider, lacking a comprehensive and decentralized method to detect abnormal behavior and adjust trust levels dynamically.
Innovation Solution
A decentralized system using client-hosted neural networks that compare behavior to a baseline, updated over time, to detect abnormal behavior and adjust client reputation, preventing interactions with clients having a bad reputation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a service provider monitors client behavior to determine user trustworthiness, then security and trust are improved, but system complexity and resource consumption increase
Solution Approach 1:
Each client device hosts its own neural network that autonomously analyzes its behavior and generates reputation scores without requiring centralized monitoring infrastructure. The system serves itself by performing trust evaluation locally, eliminating the need for complex service provider monitoring systems.
Solution Approach 2:
The centralized monitoring function is segmented and distributed to individual client devices. Each client runs its own behavior analysis neural network independently, dividing the monolithic monitoring system into autonomous units that operate in parallel without requiring complex interconnections.
2Reliability
If behavior monitoring is performed by a service provider, then abnormal behavior detection is achieved, but loss of client privacy and autonomy increases
Solution Approach 1:
Clients perform self-monitoring of their own behavior using locally-hosted neural networks. Each device analyzes its own operational patterns, application behaviors, and system events without external observation, maintaining privacy while achieving accurate abnormal behavior detection through autonomous self-assessment.
Solution Approach 2:
Instead of external entities monitoring clients, the approach is inverted so that clients monitor themselves. The direction of observation flips from service-provider-to-client to client-to-self, preserving privacy while maintaining detection capability through local behavioral analysis.
3Reliability
If traditional behavior monitoring systems are used, then security enforcement is achieved, but adaptability to new threat patterns decreases
Solution Approach 1:
The system uses neural networks with learnable parameters that automatically adapt to new threat patterns. The neural network models evolve their detection criteria based on observed behavior patterns, enabling dynamic adaptation to emerging threats without requiring manual rule updates or system reconfiguration.
Solution Approach 2:
The behavior analysis system transitions from static rule-based monitoring to dynamic neural network-based analysis. The neural networks continuously learn and update their understanding of normal versus abnormal behavior, providing adaptive security enforcement that evolves with new threat landscapes.
4Reliability
If centralized behavior analysis is performed, then consistent trust decisions are made, but processing time and network bandwidth consumption increase
Solution Approach 1:
The centralized analysis function is segmented and executed locally on each client device. Each neural network independently performs behavior analysis and generates trust decisions without requiring data transmission to a central server, eliminating network latency and enabling real-time local decision-making.
Solution Approach 2:
The neural network model acts as an intermediary that processes behavior data locally, replacing the need for direct client-to-central-server communication. This intermediary performs the analytical function distributed at the edge, reducing network dependency and processing delays.
Data Source
AI summary
Apparatuses, systems, and techniques to detect abnormal behavior on clients using one or more neural networks on said clients. In at least one embodiment, use of or behavior on one or more clients is analyzed by a first neural network to detect abnormal behavior compared to a baseline of accepted behavior, and said baseline of accepted behavior is revised over time by a second neural network based on behavior observed on said one or more clients.