Cloud Sensor Rules for Real-Time Control Plane Containment
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Solution Overview
Problem
Existing cybersecurity sensors struggle to accurately detect and classify emerging threats in real-time due to the sophistication and diversity of cyber threats, and are overwhelmed by the vast amount of data generated by modern networks, leading to challenges in identifying potential threats while avoiding false positives.
Innovation Solution
A system utilizing a sensor configured with detection rules that can be deployed in cloud computing environments to monitor events, apply detection rules, and perform mitigation actions such as halting process execution in response to potential threats, based on static analysis of code objects and real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors are used to detect cybersecurity threats, then they can monitor network activities, but they struggle to accurately detect and classify emerging threats in real-time due to the sophistication and diversity of cyberattacks
Solution Approach 1:
The sensor system dynamically adapts its detection rules and classification mechanisms based on emerging threat patterns. The sensor can update its detection algorithms in real-time to match new attack vectors, transitioning from static detection rules to dynamic, adaptive threat hunting capabilities that evolve with the threat landscape.
Solution Approach 2:
The system changes detection parameters and thresholds based on the type of threat being detected. Different detection rules are applied depending on the threat category (malware, worms, crypto miners, man-in-the-middle attacks, code injection, misconfigurations), allowing the sensor to optimize its detection accuracy for each specific threat type while maintaining broad adaptability.
2Reliability
If sensors monitor vast amounts of network data to identify potential threats, then they can detect more threats, but they generate false positives and struggle to sift through the data efficiently
Solution Approach 1:
The sensor divides network data monitoring into segmented detection rules for different threat types (malware, worms, crypto miners, man-in-the-middle attacks, code injection, misconfigurations). Each segment focuses on specific indicators of compromise, reducing the noise and false positives that come from analyzing all network data with a single detection approach.
Solution Approach 2:
The sensor acts as an intermediary layer between raw network data and threat identification. It processes and filters data through multiple detection rules before generating alerts, serving as a mediator that reduces false positives by cross-referencing multiple detection criteria before confirming a threat.
3Adaptability or versatility
If sensors deploy comprehensive detection rules to cover all threat types, then they can detect diverse threats, but the device complexity and computational overhead increase
Solution Approach 1:
The sensor is designed as a universal detection platform that handles multiple threat types (malware, worms, crypto miners, man-in-the-middle attacks, code injection, misconfigurations) through a unified architecture. Rather than requiring separate sensors for each threat type, a single sensor implements multiple detection rules that cover diverse threats, reducing overall system complexity.
Data Source
AI summary
A method and system for control plane containment of cybersecurity threat is presented. The method includes generating at least a detection rule for application by a sensor configured to detect events; configuring the sensor to deploy on a resource in a cloud computing environment, and deploying the sensor on the resource, wherein the sensor is configured to: apply the at least a detection rule on a detected event; detect an event which triggers the at least a detection rule, the event corresponding to a process running on the resource; and halt execution of the process.