Data Modeling in Power BI Introduction At a time when data is rapidly generated by different activities ranging from experiments to business, more often do we get data in different formats and structures. The data may or may not be related depending on the source. Related data in different formats and structure need to be re-organized to establish... Weiterlesen
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# Power BI Data Modelling: A Great Path to Great Analysis
Data Modeling in Power BI Introduction At a time when data is rapidly generated by different activities ranging from experiments to business, more often do we get data in different formats and structures. The data may or may not be related…
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1. Sofort-Triage & Abwehrmaßnahmen
IR-PLAYBOOK-RCEHIGH
Schritte anzeigen
SOC Incident Playbook: Remote Code Execution (RCE) Defense
1-Click Detection Engineering: Multi-Dialect SIEM Rules5 Formate
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Syntax validiert (0 Fehler)
title: Detect Exploitation - # Power BI Data Modelling: A Great Path to Great Analysis
id: 436e9af5-5111-41f1-9863-54b545d31bbc
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
- https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-26
logsource:
category: network_connection
product: any
detection:
selection:
CommandLine|contains:
- 'exploit'
condition: selection
falsepositives:
- Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
- attack.initial_accessSyntax validiert (0 Fehler)
rule CTI_Threat_Indicator {
meta:
author = "iShareStuff CTI Automated Detection Engine"
date = "2026-09-26"
description = "YARA Signature for "
strings:
$str = "# Power BI Data Modelling: A G" ascii wide
condition:
any of them
}Syntax validiert (0 Fehler)
index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("Power BI Data Modelling A Great Path to ")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - countSyntax validiert (0 Fehler)
message: "*Power BI Data Modelling A Great Path to *"Syntax validiert (0 Fehler)
CommonSecurityLog
| where Message has "Power BI Data Modelling A Great Path to "
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc2. Cyber Threat Intelligence & Forensik
🎯
MITRE ATT&CK Matrix Navigator 14 Taktiken
Reconnaissance
-
Resource Development
-
Initial Access
Execution
Persistence
-
Privilege Escalation
Defense Evasion
Credential Access
-
Discovery
-
Lateral Movement
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Collection
-
Command and Control
Exfiltration
-
Impact
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:
Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich # Power BI Data Modelling: A Great Path .... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.
🛡️ Angriffsfläche & Exposure
Netzwerk/Remote-Zugriff ohne Vorauthentifizierung möglich.
⚡ Empfohlene Sofortmaßnahmen
- 1. Perimeter-Inspektion: Relevante Portfreigaben und exponierte Endpunkte unverzüglich scannen.
- 2. Patch-Applikation: Hersteller-Hotfix einspielen oder betroffene Daemons in isolierte DMZ-Segmente überführen.
- 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
🔗 Semantisch verwandte Zero-Days
MariaDB 11.7 VEC
Synthetische RAG-Antwort