arXiv:2605.31199v1 Announce Type: cross Abstract: Capturing dynamic malware behavior in a practical but still semantically precise manner remains a significant challenge in cyber threat intelligence. While standards such as MAEC and STIX provide widely adopted vocabularies for describing malware artifacts and observations, they represent data with considerable complexity in structures that often obscure important ontological distinctions. In particular, they tend to conflate enduring malware artifacts with the events generated during execution, thereby flattening distinctions that are central
The increasing sophistication of malware and the limitations of existing analytical standards necessitate new approaches to cyber threat intelligence, making this development timely.
A more precise and modular ontology for dynamic malware analysis could significantly enhance the ability of cyber defense systems to identify, understand, and counter advanced persistent threats.
The proposed MAECO-Lite offers a method to clarify the distinction between enduring malware artifacts and ephemeral execution events, improving the semantic precision of malware descriptions.
- · Cybersecurity companies
- · National security agencies
- · Enterprises with critical infrastructure
- · Malware developers
- · Cybercriminals
Improved malware detection and response capabilities for organizations.
A potential reduction in successful cyberattacks due to better threat understanding.
Enhanced trust in digital systems and infrastructure due to stronger defenses against evolving cyber threats.
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Read at arXiv cs.AI