Neural Network Joint Representation for Security Event Prediction
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Solution Overview
Problem
Conventional intrusion detection and cybersecurity systems are rule-based, requiring constant human supervision and updates, leading to reduced reliability and high costs, especially in predicting security-related events in communication networks.
Innovation Solution
A method using a neural network that generates intermediate representations of device, user, and request features through a representation learning model, analyzing these representations to provide a joint representation for predicting events, allowing for automated decision-making and reduced human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based systems are used for intrusion detection, then prior knowledge and constant human supervision are required, but reliability is reduced and costs increase
Solution Approach 1:
The system uses self-learning algorithms that automatically adapt to new threats and patterns without requiring constant human intervention. The machine learning models continuously train on new data, enabling the system to serve itself by automatically updating its detection capabilities rather than relying on manual rule updates.
Solution Approach 2:
The patent replaces the mechanical rule-based system with an intelligent system using machine learning and neural networks. This substitution transforms the detection mechanism from static rule matching to dynamic pattern recognition, eliminating the need for manual rule configuration and improving reliability through automated adaptation.
2Reliability
If rule-based systems with constant updates are used, then detection capability is maintained, but costs and complexity increase
Solution Approach 1:
The system automatically trains on new threat data and updates its detection models without requiring manual maintenance. This self-service capability eliminates the need for continuous human intervention to update rules, reducing maintenance costs while maintaining detection capability through automated learning from new threats.
Solution Approach 2:
The system dynamically adjusts its detection parameters and thresholds based on learned patterns rather than using fixed rules. This allows the detection capability to adapt to new threats by changing operational parameters automatically, reducing the need for manual updates and lowering maintenance complexity.
3Measurement precision
If multiple feature types are integrated using neural networks, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments different feature types (device features, user features, request features) into separate processing streams before integration. This segmentation allows each feature type to be processed and encoded independently, then combined in the neural network, which improves prediction accuracy while managing computational complexity through modular processing.
Solution Approach 2:
The patent introduces intermediate representations as mediators between raw features and final predictions. These intermediate encodings transform complex multi-type features into condensed representations that capture essential patterns, improving prediction accuracy while reducing the computational burden of processing all raw features directly.
Data Source
AI summary
A method for predicting one or more events includes generating, for features of each of at least two feature types, an intermediate representation using a representation learning model for the at least two feature types. The intermediate representations of the at least two feature types are analyzed using a neural network and at least one neural network model so as to provide a joint representation for predicting certain events. One or more actions to be taken can be determined based on the one or more events predicted by the joint representation.