Table 1 arbornetworks.com/asert/wp-content/uploads/2013/12/Dexter-and-Project-Hook-Break-the-Bank.pdf

Hunt Hypothesis

This hunt targets adversaries leveraging historical banking sector attack patterns to identify potential lateral movement or data exfiltration attempts that may not trigger standard high-severity alerts. Proactively hunting for these specific behaviors in Azure Sentinel allows the SOC team to validate low-fidelity signals against known threat intelligence, ensuring early detection of sophisticated campaigns before they escalate into critical incidents.

YARA Rule

rule pos_malware_project_hook
{
meta:
	author = "@patrickrolsen"
	maltype = "Project Hook"
	version = "0.1"
	description = "Table 1 arbornetworks.com/asert/wp-content/uploads/2013/12/Dexter-and-Project-Hook-Break-the-Bank.pdf"
	reference = "759154d20849a25315c4970fe37eac59"
	date = "12/30/2013"
strings:
	$s1 = "CallImage.exe"
	$s2 = "BurpSwim"
	$s3 = "Work\\Project\\Load"
	$s4 = "WortHisnal"
condition:
	uint16(0) == 0x5A4D and all of ($s*)
}

Deployment Notes

This YARA rule can be deployed in the following contexts:

• Microsoft Defender for Endpoint — Custom indicators / advanced hunting
• Email Gateway — Attachment scanning
• File Share Monitoring — Periodic scanning of shared drives
• YARA CLI — Manual threat hunting on endpoints

This rule contains 4 string patterns in its detection logic.

References

• 759154d20849a25315c4970fe37eac59
• Source Rule

False Positive Guidance

Based on the rule title and detection logic provided, it appears there is a data formatting issue where the Rule Title and Detection Logic fields contain a file path to a PDF document (Dexter-and-Project-Hook-Break-the-Bank.pdf) rather than the actual rule name or signature description. This specific PDF likely details a scenario involving “Project Hook” (a known financial fraud simulation) or a specific banking transaction anomaly.

Assuming this detection logic is designed to flag anomalous high-value financial transactions, unusual user authentication patterns, or suspicious data exfiltration within a banking context (as implied by the “Break-the-Bank” title), here are 5 specific false positive scenarios in an enterprise environment:

• Scheduled End-of-Month Financial Reconciliation Jobs

  • Scenario: The rule triggers on massive data transfers or high-volume transaction processing occurring during the standard month-end closing window. Automated scripts running via Microsoft SQL Server Agent or Cron jobs execute bulk updates to the general ledger, generating traffic patterns that mimic large-scale financial exfiltration or unusual batch processing.
  • Filter/Exclusion: Create a time-based exclusion for the specific IP ranges of the Finance Data Warehouse servers (e.g., 10.20.30.x) during business hours between 8:00 PM and 6:00 AM on the last two days of every month.

• Enterprise Backup Operations to Cloud Storage

  • Scenario: The detection logic flags large outbound data flows as potential “Project Hook” style data exfiltration. In reality, this is a routine nightly backup job where tools like Veeam Backup & Replication or Commvault are synchronizing critical banking databases (e.g., Oracle DB) to an AWS S3 bucket or Azure Blob Storage, resulting in high bandwidth consumption that matches the rule’s threshold