Spoofed Entity Detection Through Event Distribution Complexity
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Solution Overview
Problem
Cybersecurity tools are vulnerable to spoofing attacks where malicious entities tamper with agents or centralized server components to report false information, leading to alert fatigue and delayed threat response.
Innovation Solution
Detect spoofed entities by analyzing the complexity of event distributions initiated by these entities, using machine learning and neural networks to identify patterns that deviate from normal behavior, and perform security actions to mitigate the threat.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cybersecurity tools monitor behavior of machines and users to detect malicious activity, then security detection capability is improved, but the system becomes vulnerable to spoofing attacks where malicious entities report false information
Solution Approach 1:
The system implements feedback loops where telemetry data from entities is continuously monitored, analyzed, and used to update entity profiles and detection models. The centralized server component receives telemetry, analyzes patterns, and adjusts security responses based on the complexity analysis results, creating a closed-loop feedback system that improves detection over time while maintaining reliability against spoofing attacks
Solution Approach 2:
The patent introduces an intermediary analysis layer that sits between raw telemetry data and security decisions. The centralized server component acts as a mediator that processes telemetry through complexity analysis before triggering security responses, filtering out spoofed information while preserving legitimate alerts, thus resolving the contradiction between detection sensitivity and vulnerability to false information
2Reliability
If agents gather data from machines and centralized server processes the data, then detection coverage is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system is segmented into distinct functional components: lightweight agents deployed on machines for data collection, a centralized server for processing, and distributed analysis capabilities. This segmentation allows each component to have specialized, optimized functionality, reducing overall system complexity while maintaining comprehensive detection coverage
Solution Approach 2:
The centralized server component performs multiple functions including receiving telemetry from various agents, analyzing complexity patterns, maintaining entity profiles, and coordinating security responses. This multi-functionality consolidates capabilities into a single platform, improving detection coverage without proportionally increasing system complexity
3Loss of information
If spoofed machines report false information to create false flags, then alert volume increases, but security operations center efficiency deteriorates due to alert fatigue
Solution Approach 1:
The system applies partial action by not responding to every alert with the same level of scrutiny. Instead, it uses complexity analysis to differentiate between high-value alerts requiring immediate attention and low-value spoofed alerts that can be filtered or aggregated, reducing alert fatigue while maintaining security effectiveness
Solution Approach 2:
The patent changes the parameter used for alert evaluation from simple match criteria to complexity-based scoring. By analyzing the distribution and complexity of telemetry parameters, the system transforms raw alert volume into prioritized security intelligence, improving operations center efficiency by focusing resources on high-complexity, high-probability threats
4Measurement precision
If the system analyzes complexity of event distributions to detect spoofed entities, then detection accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by establishing baseline entity profiles and expected telemetry patterns before analyzing new data. This pre-computation of normal behavior patterns allows the system to quickly compare incoming telemetry against established baselines, improving detection accuracy while reducing real-time computational energy requirements
Data Source
AI summary
Techniques are described herein that are capable of detecting a spoofed entity based on complexity of a distribution of events initiated by the spoofed entity. Frequencies with which events of event types are initiated are determined by an entity during a designated period of time. Complexity of a distribution of the events among the event types is determined based at least on the frequencies with which the events of the event types are initiated by the entity during the designated period of time. Based at least on the complexity of the distribution of the events among the event types being less than or equal to a complexity threshold, the entity is identified as a spoofed entity associated with a spoofing attack. A security action is performed with regard to the entity based at least on the entity being identified as the spoofed entity associated with the spoofing attack.