Wie Full Summary in Uncoder AI die Kusto-Abfrageanalyse für Bedrohungsjäger revolutioniert
GESCHRIEBEN VON
Steven Edwards
Technische/r Redakteur/in
[post-views]
April 23, 2025 · 5 min zu lesen
Inhaltsverzeichnis:
Die Arbeit mit Microsoft Sentinel bedeutet oft, komplexe Kusto-Abfragen zu zerlegen, insbesondere wenn subtile Angreiferverhaltensweisen verfolgt werden. Diese Abfragen können verschachtelte Logik, obskure Dateipfadroutinen und ungewöhnliche Systemereignisse enthalten, die ein tiefes Verständnis erfordern. Genau hier erstrahlt das Uncoder AI-Feature Vollständige Zusammenfassung .
Diese KI-gesteuerte Verbesserung übersetzt komplexe Microsoft Sentinel (Kusto) Erkennungslogik automatisch in eine strukturierte, leicht verständliche Erklärung – und verwandelt so komplexe Regeln innerhalb von Sekunden in umsetzbare Informationen.
Realer Fall: Clfs.sys von verdächtigen Benutzerpfaden geladen
Betrachten Sie das Beispiel aus dem Screenshot: eine Erkennungsregel, die Instanzen überwacht, in denen clfs.sys, ein legitimer Systemtreiber, aus nicht standardmäßigen oder verdächtigen Verzeichnissen wie \Users\Public\, \Temp, \Pictures, \Contactsusw. geladen wird.
Dieses Verhalten könnte darauf hinweisen, dass ein Angreifer das clfs.sys Modul aus Benutzerräumen geladen oder seitlich geladen hat, um der Erkennung zu entgehen oder unter dem Radar zu bleiben.
Aber die ursprüngliche Abfrage? Voll von verschachtelten Bedingungen, Image contains Anweisungen und Verzeichnislogik, die Zeit zum Interpretieren benötigt.
Eingabe, die wir verwendet haben (klicken, um den Text anzuzeigen)
SysmonEvent | where EventID == 7 | where (ImageLoaded endswith @’\clfs.sys’ and ((Image contains @’:\Perflogs\’ or Image contains @’:\Users\Public\’ or Image contains @’\Temporary Internet’ or Image contains @’\Windows\Temp\’) or (Image contains @’:\Users\’ and Image contains @’\Favorites\’) or (Image contains @’:\Users\’ and Image contains @’\Favourites\’) or (Image contains @’:\Users\’ and Image contains @’\Contacts\’) or (Image contains @’:\Users\’ and Image contains @’\Pictures\’)))
KI-Ausgabe (klicken, um den Text anzuzeigen)
Die bereitgestellte Microsoft Sentinel-Abfrage ist darauf ausgelegt, spezifische Systemaktivitäten zu erkennen, die durch die Systemüberwachung (Sysmon) Ereignisprotokolle erfasst werden. Hier ist eine Aufschlüsselung dessen, was diese Abfrage erreichen soll:
Filtern nach Event ID 7: Die Abfrage beginnt mit dem Filtern von Sysmon-Ereignissen, bei denen EventID == 7. In Sysmon Event ID 7 entspricht einem Image Loaded Ereignis, was bedeutet, dass das System ein neues Modul (wie eine DLL) in den Speicher geladen hat. Dies ist ein häufiges Ereignis, kann jedoch signifikant in der Identifizierung von bösartigen Aktivitäten sein, wenn ungewöhnliche oder unerwartete Module geladen werden.
Filtern nach clfs.sys: Die Abfrage wird weiter eingegrenzt, indem nach Instanzen gesucht wird, bei denen das geladene Bild ( Image Loaded Ereignisse, bei denen das geladene Bild (ImageLoaded) mit \clfs.sys. Diese Datei \clfs.sys, ist ein Windows-Systemtreiber, der mit dem Common Log File System (CLFS) in Verbindung steht. Sie ist essentiell für bestimmte Protokollierungs- und Datenbankoperationen innerhalb von Windows. Das Interesse an diesem spezifischen Treiber könnte von seinem potenziellen Missbrauch durch Malware oder andere unautorisierte Prozesse herrühren.
Spezifische Verzeichnisfilter: Die Abfrage wendet dann mehrere Filter basierend auf dem Pfad des zu ladenden Bilds (Image) an. Diese Filter suchen nach Bildern, die sich in bestimmten Verzeichnissen befinden oder Pfade enthalten, die diese Verzeichnisse enthalten. Speziell wird gesucht nach:
Pfade, die \Perflogs\beinhalten könnten Aktivitäten im Zusammenhang mit der Leistungsprotokollierung anzeigen.
Pfade, die \Users\Public\, was auf die Beteiligung des öffentlichen Benutzerverzeichnisses hindeuten könnte.
Pfade, die \Temporary Internetenthalten, was auf temporäre Internetdateien hinweisen könnte.
Pfade, die mit \Windows\Temp\enden, was auf den Windows-Ordner für temporäre Dateien hinweisen könnte, einen häufigen Ort für Malware oder nicht autorisierte Aktivitäten.
Noch spezifischere Filter werden innerhalb von Benutzerverzeichnissen (:\Users\) angewandt, die sich auf Unterverzeichnisse wie:\Favourites\ (unter Berücksichtigung verschiedener regionaler Schreibweisen)\Contacts\ \Pictures\
Diese spezifischen Verzeichnisfilter deuten darauf hin, dass die Abfrage daran interessiert ist, zu identifizieren, ob clfs.sys oder ähnliche Systemmodule von ungewöhnlichen oder potenziell kompromittierten Standorten im Benutzerspace geladen werden, was auf bösartige Aktivitäten hinweisen könnte. Der Fokus auf diese spezifischen Verzeichnisse und Unterverzeichnisse impliziert eine Sorge um die Datenintegrität, den Datenschutz oder potenzielle laterale Bewegungen innerhalb eines Netzwerks.
Im Wesentlichen ist diese Microsoft Sentinel-Abfrage dazu gedacht, verdächtiges Laden von Windows-Systemtreibern aus nicht standardmäßigen Pfaden aufzudecken, insbesondere solche, die mit Benutzerdaten, temporärer Speicherung oder anderen Bereichen verbunden sind, die typischerweise nicht in systemweiten Operationen involviert sind. Dies könnte Sicherheitsteams helfen, potenzielle Bedrohungen zu identifizieren und darauf zu reagieren, wie unautorisierter Zugriff auf sensible Daten oder bösartige Software, die versucht, sich in legitime Systemaktivitäten einzuschleichen.
Was die Vollständige Zusammenfassung lieferte
Anstatt die Logik manuell durchzugehen, generierte Uncoder AI sofort eine klare, strukturierte Erklärung:
Warum Event ID 7 wichtig ist: Sie spiegelt ein geladenes Modul wider (ImageLoaded), das harmlos sein oder bösartige Aktivitäten signalisieren kann.
Warum Clfs.sys wird angepeilt: Obwohl legitim, könnte sein Missbrauch auf laterale Bewegung oder Privilegieneskalation hinweisen.
Warum Benutzerverzeichnisse verdächtig sind: Diese Orte sind nicht typisch für systemweite Treiber und könnten auf unautorisierte Zugriffe oder Persistenztechniken hinweisen.
Betriebliche Auswirkungen: Vom Review zur Aktion
Dieses Feature verkürzte die Untersuchungszeit des Analysten drastisch – es verwandelte eine Überprüfung mit mehreren Schritten in eine einmalige Zusammenfassung. Es ermöglichte schnellere Priorisierung, besseren Kontext für die Bedrohungsvalidierung und sofortiges Rückwärtsjagen in gespeicherten Protokollen.
Kurz gesagt, schloss die Vollständige Zusammenfassung von Uncoder AI die Lücke zwischen komplexem Erkennungsengineering und schneller operativer Reaktion.
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