Infection Verification Pack Malware Detection
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Solution Overview
Problem
Legacy security solutions fail to detect sophisticated malware techniques, leading to unauthorized access, data loss, downtime, and high recovery costs due to their inability to scale across diverse operating systems and software versions.
Innovation Solution
The Infection Verification Pack (IVP) system employs behavior detonation, machine-learning classification models, and transformation of persistent artifacts to detect malware across multiple endpoint systems with different operating systems and software versions, providing automated threat detection and response with low false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If legacy security solutions use signature and heuristics matching, then they can detect known malware, but they fail to detect sophisticated malware techniques
Solution Approach 1:
The system performs preliminary behavior analysis of malware samples in isolated environments before deployment. By executing malware in controlled sandboxes and analyzing its behavior patterns, the system pre-computes behavioral signatures that can detect sophisticated malware variants without relying on traditional signature matching, thus improving both reliability and adaptability
Solution Approach 2:
The patent introduces behavioral analysis as an intermediary layer between traditional signature-based detection and sophisticated malware. This intermediary analyzes malware behavior patterns, system calls, and execution characteristics to generate behavioral signatures that bridge the gap between known and unknown malware detection methods
2Measurement precision
If security solutions analyze agent behavior in isolated context, then they can identify malware characteristics, but they fail to scale across diverse operating systems and software versions
Solution Approach 1:
The system creates universal behavioral signatures that function across multiple operating systems and software versions. By abstracting malware behavior patterns into platform-agnostic characteristics and using normalization techniques, the same detection mechanisms can be applied universally across diverse environments, maintaining both precision and scalability
Solution Approach 2:
The patent transforms behavioral data into standardized parameters that can be compared across different operating systems. By normalizing system calls, execution patterns, and behavioral metrics into universal parameter sets, the system maintains measurement precision while enabling cross-platform scaling
3Ease of manufacture
If traditional security solutions are deployed, then they provide basic malware detection, but they result in high cost to recover and repair infected computers
Solution Approach 1:
The system performs preliminary detection and analysis before malware can cause significant damage. By using behavioral analysis to identify infections early in their execution cycle, the system enables faster response and containment, reducing the overall cost and effort required for recovery and repair operations
Data Source
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AI summary
A system configured to detect malware is described. The system including an infection verification pack configured to perform behavior detonation; identify a malware object based on machine-learning; and select one or more persistent artifacts of the malware on the target system based on one or more algorithms applied to behavior traces of the malware object to select one or more persistent artifacts of the malware on the target system.