Emulated Malware Analysis for Endpoint Security Assessment
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Solution Overview
Problem
It is challenging for enterprises to assess the effectiveness of Anti-Virus (AV) and Endpoint Detection (EDR) systems against sophisticated malware attacks that employ polymorphic, oligomorphic, or metamorphic techniques to evade detection, as well as new attack combinations that may not be detected by signature or behavioral detection mechanisms.
Innovation Solution
A system for wide-scale automated testing of endpoint systems against sets of documented attack techniques and their variants, using emulated malware samples to evaluate detection results and compare different AV and security settings, allowing for the assessment of which attack paths are blocked and which remain open.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection mechanisms are used to detect malware, then detection accuracy for known malware is improved, but the system becomes ineffective against polymorphic, oligomorphic, or metamorphic malware that evades detection through code transformation
Solution Approach 1:
The patent segments malware detection into multiple independent analysis components: static analysis, dynamic analysis, behavioral analysis, and heuristic analysis. Each segment handles different aspects of malware detection, allowing the system to detect both known signatures and transformed variants through complementary approaches rather than relying on a single detection method
Solution Approach 2:
The system changes detection parameters by analyzing malware at multiple levels: code structure, execution behavior, system calls, and runtime characteristics. This multi-parameter approach allows detection of malware even when surface-level code transformations occur, as deeper structural and behavioral parameters remain consistent
2Adaptability or versatility
If behavioral detection systems are deployed to examine dynamic behavior of executing instructions, then detection of unknown malware variants is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system performs preliminary static analysis and heuristic evaluation before full dynamic behavioral analysis. This preliminary action filters out clearly benign files and identifies suspicious patterns that warrant deeper behavioral inspection, reducing the overall complexity by avoiding exhaustive dynamic analysis on all files
Solution Approach 2:
The patent introduces intermediary components such as sandbox environments, virtual machines, and controlled execution contexts that mediate between the malware sample and the host system. These intermediaries enable safe behavioral observation without requiring complex direct analysis infrastructure
3Loss of information
If comprehensive malware analysis is performed to assess security posture, then understanding of security weaknesses is improved, but time and computational resources required for assessment increase
Solution Approach 1:
The system performs partial analysis on large numbers of malware samples rather than complete analysis on fewer samples. By analyzing many samples with focused techniques (such as signature matching and quick heuristic checks), the system gathers sufficient information about security posture without the time cost of exhaustive comprehensive analysis on each sample
Solution Approach 2:
The patent uses virtual machine snapshots and environment replication to copy system states for analysis. This allows comprehensive security assessment to be performed on copied environments rather than the production system, enabling thorough analysis without time loss to system downtime or repeated setup
Data Source
AI summary
At least in some examples, a non-transitory machine-readable storage medium can be encoded with instructions for analysing an operating system configuration, the instructions executable by a processor of a system, whereby to cause the system to generate a set of malware samples, each malware sample defined by a set of actions forming an attack chain representing a sequence of procedures, execute each of the malware samples on an instance of the operating system configuration to generate a set of logs, and using the set of logs, determine a set of actions detected by an anti-malware process of the operating system configuration.