Multi-Architecture Cybersecurity Sensor for Real-Time Threat Detection
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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 they are overwhelmed by the vast volume of data, while compromised sensors can provide misleading information or serve as entry points for attackers.
Innovation Solution
A multi-architecture sensor system is deployed on a hardware abstraction layer of a virtual instance, utilizing a monitoring debugger and sensor application to detect events, determine cybersecurity issues, and initiate remediation actions, with features like static analysis of disks and eBPF protocol for enhanced detection and communication with a sensor backend server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors are used to detect cybersecurity threats, then the system can identify malicious activities, but the sensors struggle to keep pace with rapidly evolving cyberattacks and cannot accurately detect emerging threats in real-time
Solution Approach 1:
The sensor is divided into multiple independent architecture components (eBPF-based sensor, Linux kernel sensor, Windows sensor) that can operate autonomously. Each architecture sensor is specialized for detecting specific types of threats on its respective platform, enabling accurate real-time detection without requiring a single monolithic sensor to handle all threat types simultaneously.
Solution Approach 2:
The multi-architecture sensor system provides universal threat detection capability across diverse operating systems and platforms. The sensor framework can deploy different architecture sensors (eBPF, Linux kernel, Windows) to detect various cyber threats including malware, ransomware, and advanced persistent threats across heterogeneous environments, making the system adaptable to evolving attack vectors.
2Measurement precision
If sensors monitor all network data to identify threats, then comprehensive threat detection is achieved, but the sheer volume of data overwhelms the sensors and increases false positives
Solution Approach 1:
Each architecture sensor processes data locally with specialized filtering capabilities tailored to its architecture type. The eBPF sensor filters network packets at the kernel level, the Linux kernel sensor monitors system calls locally, and the Windows sensor filters Windows event logs locally. This local quality approach reduces data volume before transmission while maintaining comprehensive threat detection coverage.
Solution Approach 2:
The system extracts only relevant threat indicators from vast amounts of data at the source architecture level. Each sensor architecture extracts suspicious patterns, anomalies, and threat signatures locally, transmitting only these extracted indicators to the backend server rather than transmitting all raw data, thereby improving processing efficiency while maintaining detection comprehensiveness.
3Reliability
If sensors are deployed directly in the computing environment to detect threats, then real-time monitoring is achieved, but compromised sensors can provide misleading information or serve as entry points for attackers
Solution Approach 1:
The multi-architecture sensor system introduces an intermediary backend server that receives, validates, and cross-correlates data from multiple independent architecture sensors. This intermediary architecture prevents single-point compromise because an attacker would need to compromise multiple diverse sensor architectures simultaneously to successfully deceive the system, thereby maintaining reliable monitoring while reducing sensor compromise risk.
4Adaptability or versatility
If multiple sensor architectures are deployed to detect diverse threats, then detection coverage is improved, but the device complexity increases
Solution Approach 1:
The system merges multiple architecture sensors (eBPF, Linux kernel, Windows) into a unified multi-architecture sensor framework that shares common backend infrastructure, data formats, and threat intelligence databases. This merging approach maintains diverse detection coverage across different operating systems while reducing overall system complexity through standardized interfaces and centralized management.
Data Source
AI summary
A system and method for deploying a multi-architecture sensor for cybersecurity object detection is presented. The method includes: deploying a monitoring debugger on a hardware abstraction layer of a virtual instance, the virtual instance deployed in a computing environment; deploying a sensor application communicating with the monitoring debugger, the sensor application configured to detect events in the virtual instance; detecting an event based on data detected by the sensor application; determining that the detected event corresponds to a cybersecurity issue; and initiating a remediation action based on the cybersecurity issue.