Ransomware attacks can be difficult to detect in real-time, as they often involve the attacker encrypting or altering data in a way that is not immediately noticeable. However, there are a few approaches you can take to try to detect ransomware attacks using KQL in real-time.
One approach is to look for unusual changes to your system or data. For example, you could set up an alert to trigger when there is a sudden increase in the number of file deletion or modification events, or when there is a sudden change in the access patterns of certain files or directories.
Here is an example KQL query that you could use to set up an alert for unusual file activity:
SecurityEvent
| where EventID in (4656, 4658, 4660)
| summarize count() by AccountName, bin(TimeGenerated, 1h)
| where count_ > 50
This query counts the number of file deletion, modification, and access events by account name and groups them into 1-hour bins. It then filters the results to only include bins where the count is greater than 50, indicating a high level of activity in a short period of time.
Another approach is to look for signs of network communication with known ransomware command and control (C2) servers. You can use KQL to query your network traffic logs for connections to known C2 servers, and set up an alert to trigger if such connections are detected.
Here is an example KQL query that you could use to detect connections to known C2 servers:
NetworkConnection
| where RemoteIP in (list of known C2 IP addresses)
| summarize count() by bin(TimeGenerated, 1h)
| where count_ > 0
This query counts the number of connections to known C2 servers and groups them into 1-hour bins. It then filters the results to only include bins where the count is greater than 0, indicating that at least one connection to a known C2 server was detected.
It’s important to note that these queries are just examples, and you will need to customize them to fit your specific environment and needs. I recommend reviewing the documentation provided by Azure Monitor for more information on the different types of data that are available in Azure Monitor logs, as well as the various operators and functions that you can use in your KQL queries.