Baseline-Based Sensor Detection for Cloud Cybersecurity Noise
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Solution Overview
Problem
Existing cybersecurity threat detection solutions for cloud computing environments face challenges due to the impracticality of agent-based solutions, which require heavy resource usage and root privileges, and the incompleteness of agentless solutions, which lack real-time threat detection and runtime data, leading to undetected threats and high computational costs.
Innovation Solution
A system deploying sensors on resources to listen to data link layer events, combining runtime and static analysis to detect cybersecurity threats, reducing overlap and resource usage by utilizing a sensor backend server to initiate inspections efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If agent based solutions are deployed to detect cybersecurity threats, then detection completeness is improved, but resource consumption increases
Solution Approach 1:
A sensor backend server is introduced as an intermediary component that performs the computationally intensive analysis of runtime data. The sensors deployed on endpoint devices collect data and forward it to the backend server, which then performs the heavy lifting of threat detection through static analysis and runtime data correlation. This mediator architecture allows lightweight sensors to achieve comprehensive detection capabilities without consuming excessive local resources.
Solution Approach 2:
Instead of deploying full agent functionality to each endpoint, the system uses lightweight sensors that copy only the essential data collection functionality. The comprehensive detection capability is replicated on the sensor backend server, which processes data from multiple sensors centrally. This copying approach distributes the detection intelligence to where it's needed while maintaining resource efficiency.
2Reliability
If agent based solutions are deployed to detect cybersecurity threats, then detection completeness is improved, but system complexity increases
Solution Approach 1:
The system segments the threat detection functionality into two distinct components: lightweight sensors deployed on endpoint devices for data collection, and a centralized sensor backend server for analysis and detection. This segmentation separates the simple data gathering task from the complex analysis task, reducing the complexity burden on individual endpoint devices while maintaining comprehensive detection capability through the centralized server.
3Use of energy by moving object
If agentless solutions are deployed to detect cybersecurity threats, then resource consumption is reduced, but detection completeness deteriorates
Solution Approach 1:
The sensor backend server provides universal detection capabilities that serve multiple functions: it performs static analysis of collected data, correlates runtime data from multiple sources, maintains baselines for anomaly detection, and generates security findings. This multi-functional centralized server enables lightweight sensors to achieve comprehensive detection without requiring complex local agents, thus maintaining resource efficiency while improving detection completeness.
4Reliability
If both agent and agentless solutions are deployed to detect cybersecurity threats, then detection completeness is improved, but computational cost increases
Solution Approach 1:
The system merges the strengths of both agent-based and agentless approaches into a unified architecture. Lightweight sensors (agentless) collect runtime data from endpoint devices, while the sensor backend server performs comprehensive analysis by combining this runtime data with static analysis of security configurations and threat intelligence. This merging eliminates the need to deploy separate full-featured agents, reducing computational costs while maintaining the detection completeness benefits of both approaches.
Data Source
AI summary
A system and method for reducing false positive detection of cybersecurity events is disclosed. The method includes: configuring a plurality of resources to deploy a sensor, each sensor configured to listen on a data link layer for an event; receiving from each sensor a plurality of events, each event including an event type; generating a group of resources having a common attribute; generating a noise metric for the group of resources based on a number of events of an event type; generating a threshold based on the noise metric; configuring each sensor of a resource from the group of resources to detect a number of events exceeding the threshold; detecting a cybersecurity event in response to determining that a first resource from the group of resources has a number of events of a first type exceeding the threshold; and initiating a mitigation action based on the detected cybersecurity event