Cybersecurity System Using Event Lattices for Threat Detection
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Solution Overview
Problem
Current cybersecurity systems are inadequate in protecting networked computer systems from sophisticated threats and attacks, as they fail to effectively detect and mitigate risks in real-time, leading to vulnerabilities in sensitive information storage and transmission.
Innovation Solution
A cybersecurity system comprising sensors, inference servers, and monitoring servers that utilize event lattices and machine learning techniques, such as Sequence Learning and Formal Concept Analysis, to detect and predict behavior patterns indicative of potential threats, allowing for real-time monitoring and mitigation of risks across networked devices and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cyber security measures are used, then basic protection is provided, but they fail to detect sophisticated threats in real-time
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing event data from multiple sources, building behavioral profiles and detection models in advance. This allows the system to quickly identify and respond to sophisticated threats without time delays, as the analytical framework is already established before attacks occur.
Solution Approach 2:
The system implements dynamic adaptation by continuously updating behavioral profiles and detection models based on new event data and emerging threat patterns. This dynamic approach enables real-time detection of sophisticated threats while maintaining reliability, as the system evolves alongside changing attack methodologies.
2Measurement precision
If comprehensive monitoring of all network events is implemented, then threat detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex monitoring task by dividing event data collection and analysis across multiple distributed components, including event sensors, inference servers, and behavioral profile databases. This segmentation maintains high detection accuracy through comprehensive monitoring while reducing individual component complexity and enabling parallel processing.
Solution Approach 2:
The system implements universal event processing capabilities that handle multiple types of events and threat scenarios through a unified behavioral analysis framework. This multi-functionality approach improves detection accuracy across diverse threats without proportionally increasing system complexity, as the same core mechanisms apply to various event types.
3Speed
If real-time analysis of all event data is performed, then threat identification speed improves, but computational resources are consumed
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing specific behavioral patterns and event sequences that are most indicative of threats, rather than uniformly processing all event data with equal intensity. This approach maintains fast threat identification speed while reducing overall computational energy consumption by prioritizing high-value analysis targets.
Solution Approach 2:
The system uses copying by creating and maintaining behavioral profiles that represent normal and anomalous patterns, allowing rapid comparison against new events without re-analyzing entire historical datasets. This copying mechanism enables fast threat identification while significantly reducing computational energy requirements compared to full data re-processing.
Data Source
AI summary
A cyber security system includes a plurality of event sensors to detect events, a plurality of inference servers, and a server in communication with the plurality of inference servers. Each inference server of the plurality is in communication with a subset of event sensors of the plurality of event sensors. Each inference server has a portion of an event lattice and is to compare the event detected by the subset of event sensors to the event lattice. Each inference server is to identify an originator having a behavior pattern indicative of an attack and communicating an identifier associated with the originator. The server is to provide an interface indicating the behavior pattern indicative of an attack and the identifier of the originator.