Definition
Analysis of source files, processes, destination files, or destination servers associated with a scheduled job to detect unauthorized use of job scheduling.
How it works
Scheduled job execution can be utilized by adversaries for the purpose of persistence, conducting remote execution, or gaining privileges. Details of a scheduled job such as associated source files, processes, destination files, or destination servers are first identified and analyzed and then compared against an anti-malware signature database, whitelist, or reputation server. For example, a file associated with a scheduled job to be executed at a specified time or a remote server that is accessed as part of a scheduled task is compared against an anti-malware signature database, whitelist, or reputation server, and if a match is found, execution is denied and an alert is generated.
In addition to traditional scheduled jobs, triggers can be set to execute a specific command after detecting a specific event in the system, such as with WMI Event Subscriptions in Windows.
Considerations
Jobs can be scheduled in many different and sometimes creative ways through operating system capabilities.
Implementation perspective
Scheduled Job Analysis 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 Job Schedule.
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.
Explicit technique relationships
Show inferred artifact relationship paths (1)
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.
Authoritative sources
- Open this technique on the official D3FEND website ↗
- Open the official ontology resources ↗
- CAR-2013-05-004: Execution with AT ↗ExternalKnowledgeBase
- CAR-2013-08-001: Execution with schtasks ↗ExternalKnowledgeBase
- Preventing execution of task scheduled malware ↗McAfee LLC · Anil Ramabhatta, Harinath Vishwanath Ramachetty, Nandi Dharma Kishore · PatentReference
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