Definition
Employing a pattern matching rule language to analyze the content of files.
How it works
Rules, often called signatures, are used for both generic and targeted malware detection. The rules are usually expressed in a domain specific language (DSL), then deployed to software that scans files for matches. The rules are developed and broadly distributed by commercial vendors, or they are developed and deployed by enterprise security teams to address highly targeted or custom malware. Conceptually, there are public and private rule sets. Both leverage the same technology, but they are intended to detect different types of cyber adversaries.
Considerations
- Patterns expressed in the DSLs range in their complexity. Some scanning engines support file parsing and normalization for high fidelity matching, others support only simple regular expression matching against raw file data. Engineers must make a trade-off in terms of:
- The fidelity of the matching capabilities in order to balance high recall with avoiding false positives,
- The computational load for scanning, and
- The resilience of the engine to deal with adversarial content presented in different forms-- content which in some cases is designed to exploit or defeat the scanning engines.
- Signature libraries can become large over time and impact scanning performance.
- Some vendors who sell signatures have to delete old signatures over time.
- Simple signatures against raw content cannot match against encoded, encrypted, or sufficiently obfuscated content.
Implementations
- YARA
- ClamAV
Implementation perspective
File Content Rules should be treated as a technical defensive capability rather than a product checkbox. In practice, teams should define the protected scope, the conditions under which the technique acts, and the observable evidence that demonstrates the intended behavior. For this technique, likely engineering context includes File.
Use the technique to identify suspicious, unauthorized, or abnormal activity through observable evidence and repeatable analysis.
Questions to ask
- Which events, states, or artifacts must be observed for the analysis to work?
- What analytic logic, threshold, comparison, or signature turns observations into a finding?
- How are expected false positives, blind spots, and environmental variations documented?
- Who receives the result, and what action is expected when the technique produces a finding?
Evidence and validation
- Telemetry and data-source configuration records
- Analytic logic, thresholds, signatures, and version history
- Test cases demonstrating expected positive and negative results
- Alert, triage, escalation, and tuning records
Common failure patterns
- Required telemetry is missing, delayed, or transformed in a way that invalidates the analysis.
- The technique produces alerts without an accountable triage and response process.
- Detection coverage is claimed from product deployment without testing the relevant analytic behavior.
This implementation perspective is original Bare Metal Cyber educational content. It does not replace the official D3FEND definition or establish that a specific product implements the technique.
Technique hierarchy
Top-level family
Parent techniques
Direct child techniques
None listed at this level.
Artifacts and ontology entities
These relationships describe how D3FEND connects a defensive technique to artifacts or other ontology entities. They describe graph semantics, not a product certification.
Show inferred artifact relationship paths (1)
NIST SP 800-53 relationships
The relation label is preserved from the D3FEND ontology. It is not converted into a claim that the control automatically implements or validates this technique.
ATT&CK Enterprise mitigation relationships
These links come from the D3FEND ontology’s ATT&CK mitigation mapping. They help users navigate between the knowledge bases and do not guarantee mitigation effectiveness.
D3FEND note: Process Analysis and subclasses.
Offensive-technique relationships
These relationships are generated from D3FEND graph paths and are explicitly experimental. They should be treated as hypotheses for defensive analysis—not as proof that the technique prevents, detects, or removes an offensive behavior.
ATT&CK Enterprise (99)
Showing the first 60 of 99 source-derived relationships. Open the official D3FEND technique for the current graph view.
ATT&CK for ICS (8)
Authoritative sources
- Open this technique on the official D3FEND website ↗
- Open the official ontology resources ↗
- Computational modeling and classification of data streams ↗Crowdstrike Inc · Sven Krasser; David Elkind; Patrick Crenshaw; Brett Meyer · PatentReference
- Detecting script-based malware ↗Crowdstrike Inc · Ion-Alexandru IONESCU; Satoshi Tanda · PatentReference
- Distributed meta-information query in a network ↗Bit 9 Inc · Todd Brennan; John Hanratty · PatentReference
- System and methods thereof for logical identification of malicious threats across a plurality of end-point devices (epd) communicatively connected by a network ↗Palo Alto Networks IncCyber Secdo Ltd · Gil BARAK · PatentReference
Bare Metal Cyber is an independent educational publisher and is not affiliated with or endorsed by The MITRE Corporation. MITRE D3FEND™ and the D3FEND logo are trademarks of The MITRE Corporation. MITRE ATT&CK® and ATT&CK® are registered trademarks of The MITRE Corporation. Use of D3FEND source material is subject to the official Terms of Use.