Agent-Less Remote Threat Analysis Across Operating Systems
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Solution Overview
Problem
Existing systems face challenges in effectively protecting enterprise computing systems from sophisticated malicious threats and identifying vulnerabilities across diverse operating systems, as conventional threat detection methods are limited to specific OS platforms and struggle with advanced, dormant malware that evades traditional antivirus and intrusion detection systems.
Innovation Solution
A remote threat analysis system that deploys a unified threat analysis tool capable of operating across multiple operating systems (Windows, UNIX, MacOS), collecting and analyzing system information for potential threats, including memory data, and identifying sensitive data, while providing real-time forensic analysis and proactive threat notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional threat detection methods are used, then detection simplicity is maintained, but detection capability against sophisticated malware is insufficient
Solution Approach 1:
The system segments threat detection into multiple analysis layers: static analysis of code characteristics, dynamic analysis of runtime behavior, and heuristic analysis of suspicious patterns. Each layer handles specific aspects of malware detection, allowing the system to achieve comprehensive detection capability while maintaining manageable complexity through modular organization of detection functions.
Solution Approach 2:
The threat detection system is designed as a universal platform that can detect multiple types of threats (viruses, worms, trojans, ransomware, spyware) across different operating systems (Windows, UNIX, MacOS) using a single unified architecture. This multi-functionality approach enables the system to handle diverse threat scenarios without requiring separate specialized systems for each threat type or platform.
2Reliability
If traditional antivirus software is deployed, then ease of operation is maintained, but ability to detect dormant malware is insufficient
Solution Approach 1:
The system performs preliminary static analysis of files and code segments before they are executed or activated. By analyzing code characteristics, headers, and structural patterns in advance, the system can identify dormant malware and malicious scripts before they become active threats, enabling detection of threats that traditional antivirus software would miss while maintaining automated operation.
Solution Approach 2:
The system introduces heuristic analysis as an intermediary layer between traditional signature-based detection and complex behavioral monitoring. This intermediary approach uses rules-based analysis of suspicious patterns and characteristics to detect malware that lacks known signatures, providing enhanced detection reliability while operating automatically without requiring manual intervention or complex user configuration.
3Adaptability or versatility
If platform-specific threat detection tools are used, then detection accuracy for specific OS is improved, but cross-platform coverage is reduced
Solution Approach 1:
The system implements a universal threat detection platform that operates across Windows, UNIX, and MacOS by abstracting OS-specific detection mechanisms into a common architecture. The core detection engine uses platform-independent analysis methods for code structure, while OS-specific modules handle platform-particular characteristics, enabling both broad cross-platform coverage and maintained detection accuracy for each specific operating system.
Solution Approach 2:
The system applies local quality by tailoring detection strategies to specific operating system characteristics while maintaining a unified overall approach. Each OS platform receives customized detection rules and analysis methods appropriate to its specific architecture and common threat vectors, while the central coordination layer ensures consistent cross-platform operation and data aggregation, achieving both platform optimization and universal compatibility.
4Measurement precision
If comprehensive system scanning is performed, then threat identification capability is improved, but analysis time is increased
Solution Approach 1:
The system implements risk-based prioritization that performs comprehensive analysis on high-risk targets while applying lighter scanning to low-risk systems. Critical infrastructure and systems showing suspicious indicators undergo full comprehensive scanning, while previously verified secure systems receive periodic light-touch monitoring, reducing overall analysis time while maintaining high vulnerability identification accuracy for the most important assets.
Solution Approach 2:
The scanning process is segmented into multiple phases: initial rapid assessment identifying obvious threats, targeted deep analysis of suspicious areas, and comprehensive verification only where needed. This phased segmentation allows the system to quickly screen large numbers of systems, focus detailed analysis on high-priority targets, and maintain high vulnerability detection accuracy without uniformly applying time-consuming comprehensive scanning to all systems.
Data Source
AI summary
Embodiments of the present invention provide techniques, systems, and methods for remote, agent-less enterprise computer threat data collection, malicious threat analysis, and identification and reporting of potential and real threats present on an enterprise computer system. Specifically, embodiments are directed to a system that securely collects system information from computers across the enterprise, internally encrypts and analyzes the collected information for indicators of compromise, threatening behavior, and known vulnerabilities, and generates alerts regarding known and potential threats for further analysis and remediation. If potential threats are identified, the system may deploy a memory analysis module that takes a deeper analysis of the potentially compromised computer to obtain more information about the potential threat. The remote, agent-less collection, analysis, and identification process can be repeated periodically to obtain additional information over time in order to identify the nature of the threat, and may delete itself after completion to avoid detection.