Authentication From Suspicious DeviceName

KQL Queries

Using KQL (Kusto Query Language) query to identify suspicious authentication attempts originating from unusual or suspicious workstation names. This query assumes you're working with Azure Monitor, Sentinel, or a similar platform that supports KQL and logs such as SecurityEvent, SigninLogs, or other authentication-related logs.

SecurityEvent

Using KQL (Kusto Query Language) query to identify suspicious authentication attempts originating from unusual or suspicious workstation names. This query assumes you're working with Azure Monitor, Sentinel, or a similar platform that supports KQL and logs such as SecurityEvent, SigninLogs, or other authentication-related logs.

// Define a list of known suspicious workstation patterns
let SuspiciousWorkstations = dynamic(["UNKNOWN", "TEMP", "WORKSTATION-", "DESKTOP-"]);
// Query authentication logs
SecurityEvent
| where EventID in (4624, 4625)  // Filter for logon success (4624) or failure (4625) events
| extend WorkstationName = iff(isnotempty(Workstation), Workstation, Computer) // Extract workstation/computer name
| where WorkstationName has_any (SuspiciousWorkstations) 
      or WorkstationName matches regex @"^(TEMP|DESKTOP|UNKNOWN|WORKSTATION-).*$"  // Match dynamic list or regex patterns
| summarize
    LogonAttempts = count(),
    UniqueUserCount = dcount(TargetUserName),
    FailedAttempts = countif(EventID == 4625)
    by WorkstationName, TargetUserName, LogonType, bin(TimeGenerated, 1h)
| extend LogonTypeDescription = case(
    LogonType == 2, "Interactive",
    LogonType == 3, "Network",
    LogonType == 10, "Remote Interactive",
    LogonType == 7, "Unlock",
    LogonType == 5, "Service",
    LogonType == 4, "Batch",
    "Unknown"
)
| order by LogonAttempts desc
| project TimeGenerated, WorkstationName, TargetUserName, LogonTypeDescription, LogonAttempts, FailedAttempts, UniqueUserCount

Key Details:

1. Dynamic List of Suspicious Names: Adjust SuspiciousWorkstations to include prefixes, patterns, or specific workstation names you consider suspicious.

2. Event IDs: Targets Windows Security Event IDs for logon success (4624) and failure (4625).

3. Regex Matching: Matches patterns using a regex for flexible detection of workstation naming conventions.

4. Summarization: Group data by workstation name, user, and logon type for better analysis and filtering.

5. Logon Type Mapping: Provides a human-readable description of the logon type for better context.

Results:

• WorkstationName: The suspicious workstation name.

• TargetUserName: User attempting to log in.

• LogonTypeDescription: Describes how the logon was attempted (e.g., Interactive, Network, Remote Interactive).

• LogonAttempts: Total authentication attempts.

• FailedAttempts: Count of failed logon attempts.

You may have to tweak the query to include additional suspicious patterns or integrate it with threat intelligence feeds for enhanced correlation.

DeviceLogonEvents

To discover authentication events originating from suspicious workstation names using the DeviceLogonEvents table in Microsoft Sentinel or another Log Analytics environment:

// Define a list of suspicious workstation patterns
let SuspiciousWorkstations = dynamic(["UNKNOWN", "TEMP", "WORKSTATION-", "DESKTOP-"]);
// Query DeviceLogonEvents table
DeviceLogonEvents
| extend WorkstationName = iff(isnotempty(DeviceName), DeviceName,
AccountName) // Normalize workstation/computer name
| where WorkstationName has_any (SuspiciousWorkstations) 
      or WorkstationName matches regex @"^(TEMP|DESKTOP|UNKNOWN|WORKSTATION-).*$"  // Match dynamic list or regex patterns
| summarize
    LogonAttempts = count(),
    FailedAttempts = countif(ActionType == "LogonFailed"),
    SuccessfulAttempts = countif(ActionType == "LogonSuccess"),
    UniqueUserCount = dcount(AccountName)
    by WorkstationName, AccountName, LogonType, bin(Timestamp, 1h)
| extend LogonTypeDescription = case(
    LogonType == "Interactive", "Interactive",
    LogonType == "Network", "Network",
    LogonType == "RemoteInteractive", "Remote Interactive",
    LogonType == "Service", "Service",
    LogonType == "Batch", "Batch",
    LogonType == "Unlock", "Unlock",
    "Unknown"
)
| order by LogonAttempts desc
| project Timestamp, WorkstationName, AccountName, LogonTypeDescription, LogonAttempts, SuccessfulAttempts, FailedAttempts, UniqueUserCount

Explanation of the Query:

1. Suspicious Patterns:

   • SuspiciousWorkstationsdefines known suspicious naming patterns (e.g., TEMP, UNKNOWN, etc.).

   • Includes has_any for quick matching and regex for flexible pattern detection.

2. Table and Fields:

   • The DeviceLogonEventstable is queried.

   • Extracts workstation or device names using DeviceName or AccountName

3. Filters:

   • Filters records with workstation names matching the suspicious patterns.

4. Aggregation:

   • Groups result by workstation name, account name, and logon type.

   • Summarises total logon attempts, failed attempts, successful attempts, and unique user count.

5. Logon Type Mapping:

   • Maps the LogonType to descriptive text for better interpretation.

6. Output:

   • Results are sorted by the number of logon attempts and projected to show:

     • Timestamp, WorkstationName, AccountName, LogonTypeDescription, TotalAttempts, SuccessfulAttempts, FailedAttempts, and UniqueUserCount.

Customisation:

• Additional Filters: Add conditions for specific user accounts, logon times, or suspicious IPs if required.

• Time Binning: Adjust bin(Timestamp, 1h) to a different interval (e.g., 5 minutes or daily).

• Threat Intelligence Integration: Correlate workstation names with known threat actor tools or tactics.

IdentityLogonEvents

Using KQL query to discover authentication attempts from suspicious workstation names using the IdentityLogonEvents table in Microsoft Sentinel or Log Analytics:

// Define a list of suspicious workstation patterns
let SuspiciousWorkstations = dynamic(["UNKNOWN", "TEMP", "WORKSTATION-", "DESKTOP-"]);
// Query IdentityLogonEvents table
IdentityLogonEvents
| extend WorkstationName = iff(isnotempty(DeviceName), DeviceName, TargetDeviceName) // Normalize workstation/device field
| where WorkstationName has_any (SuspiciousWorkstations)
      or WorkstationName matches regex @"^(TEMP|DESKTOP|UNKNOWN|WORKSTATION-).*$"  // Match patterns or dynamic list
| summarize
    TotalAttempts = count(),
    FailedAttempts = countif(ActionType == "LogonFailed"),
    SuccessfulAttempts = countif(ActionType == "LogonSuccess"),
    UniqueUsers = dcount(AccountDisplayName)
    by WorkstationName, AccountDisplayName, LogonType, bin(Timestamp, 1h)
| extend LogonTypeDescription = case(
    LogonType == "Interactive", "Interactive",
    LogonType == "RemoteInteractive", "Remote Interactive",
    LogonType == "Network", "Network",
    LogonType == "Batch", "Batch",
    LogonType == "Service", "Service",
    "Unknown"
)
| order by TotalAttempts desc
| project Timestamp, WorkstationName, AccountDisplayName, LogonTypeDescription, TotalAttempts, SuccessfulAttempts, FailedAttempts, UniqueUsers

Key Features of the Query:

1. Suspicious Patterns:

   • SuspiciousWorkstationsincludes known suspicious naming conventions (e.g., TEMP, WORKSTATION-).

   • Matches using has_any and regex for flexible detection.

2. Field Normalisation:

   • Uses DeviceName or TargetDeviceName to extract workstation or device information.

3. Logon Event Filtering:

   • Focuses on logon events where the workstation name matches suspicious patterns.

4. Aggregation:

   • Summarises:

     • TotalAttempts: Total logon attempts.

     • FailedAttempts: Count of failed logon attempts.

     • SuccessfulAttempts: Count of successful logon attempts.

     • UniqueUsers: Count of distinct users attempting logons.

5. Logon Type Mapping:

   • Converts raw LogonType values into meaningful descriptions.

6. Results:

   • Sorted by total attempts and includes:

     • Timestamp, WorkstationName, AccountDisplayName, LogonTypeDescription, TotalAttempts, SuccessfulAttempts, FailedAttempts, and UniqueUsers.

Customisation Options:

• Time Filtering: Add | where Timestamp between (startTime .. endTime) to restrict the timeframe.

• Additional Filters: Add filters for specific user accounts, IP addresses, or logon statuses.

• Suspicious Patterns: Update SuspiciousWorkstations to include organisation-specific patterns or known attacker conventions.

SigninLogs

Using KQL query to discover authentication attempts from suspicious workstation names using the SigninLogs table in Microsoft Sentinel or Azure Log Analytics:

// Define a list of suspicious workstation patterns
let SuspiciousWorkstations = dynamic(["UNKNOWN", "TEMP", "WORKSTATION-", "DESKTOP-"]);
// Query SigninLogs table
SigninLogs
| extend WorkstationName = iff(isnotempty(DeviceDetail.deviceDisplayName), tostring(DeviceDetail.deviceDisplayName), tostring(DeviceDetail.operatingSystem)) // Extract and cast to string
| where WorkstationName has_any (SuspiciousWorkstations)
      or WorkstationName matches regex @"^(TEMP|DESKTOP|UNKNOWN|WORKSTATION-).*$"  // Match dynamic list or regex patterns
| summarize
    TotalAttempts = count(),
    FailedAttempts = countif(ResultType != "0"), // Non-zero ResultType indicates failure
    SuccessfulAttempts = countif(ResultType == "0"),
    UniqueUsers = dcount(UserPrincipalName)
    by tostring(WorkstationName), UserPrincipalName, AppDisplayName, bin(TimeGenerated, 1h)
| order by TotalAttempts desc
| project TimeGenerated, WorkstationName, UserPrincipalName, AppDisplayName, TotalAttempts, SuccessfulAttempts, FailedAttempts, UniqueUsers

Explanation:

1. Suspicious Patterns:

   • SuspiciousWorkstations Defines workstation name patterns commonly linked to suspicious activity.

   • Uses has_any for a quick match and regex for flexible pattern matching.

2. Field Normalisation:

   • Extracts workstation name from DeviceDetail.deviceDisplayName or falls back to DeviceDetail.operatingSystem if empty.

3. Logon Results:

   • ResultType == "0" Indicates successful logons.

   • Any other ResultType is treated as a failure.

4. Aggregation:

   • Groups by WorkstationName, UserPrincipalName, and AppDisplayName.

   • Summarizes:

     • TotalAttempts: Total authentication attempts.

     • FailedAttempts: Count of failed logons.

     • SuccessfulAttempts: Count of successful logons.

     • UniqueUsers: Count of distinct users.

5. Time Binning:

   • Bins results into 1-hour intervals using bin(TimeGenerated, 1h).

6. Results:

   • Sorted by TotalAttemptsand displays:

     • TimeGenerated, WorkstationName, UserPrincipalName, AppDisplayName, TotalAttempts, SuccessfulAttempts, FailedAttempts, and UniqueUsers.

Customisation:

• Patterns: Update SuspiciousWorkstations to include organisation-specific suspicious workstation naming conventions.

• Time Range: Add a time filter using | where TimeGenerated between (datetime(YYYY-MM-DD HH:MM:SS) .. datetime(YYYY-MM-DD HH:MM:SS)).

• Additional Fields: Extend with other fields from SigninLogs for more context, such as IP addresses or location data.

Splunk Query:

Wineventlog

Using Splunk query to discover authentication events originating from suspicious workstation names. This query assumes you're using Windows Event Logs (index=wineventlog) or a similar data source for authentication events.

Note: The fields in your Splunk logs may differ slightly; for example, AccountName may be displayed as Account_Name.

index=wineventlog EventCode IN (4624, 4625) 
| eval WorkstationName=coalesce(Workstation, ComputerName)  // Normalize workstation field names
| search WorkstationName IN ("TEMP*", "DESKTOP-*", "UNKNOWN", "WORKSTATION-*")  // Match known suspicious patterns
| eval Suspicious = if(match(WorkstationName, "^(TEMP|DESKTOP|UNKNOWN|WORKSTATION-).*$"), "Yes", "No")
| stats count AS TotalAttempts, 
        count(eval(EventCode=4625)) AS FailedAttempts, 
        dc(UserName) AS UniqueUsers 
        by WorkstationName, UserName, LogonType, Suspicious
| eval LogonTypeDescription=case(
        LogonType=="2", "Interactive",
        LogonType=="3", "Network",
        LogonType=="10", "Remote Interactive",
        LogonType=="7", "Unlock",
        LogonType=="5", "Service",
        LogonType=="4", "Batch",
        true(), "Unknown"
    )
| where Suspicious="Yes" 
| sort - TotalAttempts
| table WorkstationName, UserName, LogonTypeDescription, TotalAttempts, FailedAttempts, UniqueUsers

Key Details:

1. EventCode Filtering:

   • 4624: Logon success.

   • 4625: Logon failure.

2. Normalization:

   • Uses coalesce() to handle scenarios where either Workstation or ComputerName maybe populated.

3. Suspicious Workstation Patterns:

   • Matches common suspicious workstation naming conventions like TEMP*, DESKTOP-*, UNKNOWN, or WORKSTATION-*.

   • Regex patterns used in match() provide flexibility.

4. Stats Aggregation:

   • Aggregates data to summarise:

     • TotalAttempts: Total authentication attempts.

     • FailedAttempts: Failed login attempts.

     • UniqueUsers: Unique users attempting to log in from suspicious workstations.

5. Logon Type Description:

   • Maps LogonType values to human-readable descriptions for better context.

6. Filtering and Sorting:

   • Displays results only for suspicious workstations (where Suspicious="Yes").

   • Sorts results by the highest number of total attempts.

7. Output:

   • Displays key fields like WorkstationName, UserName, LogonTypeDescription, and summarised stats.

Customisation:

• Suspicious Patterns: Adjust the search clause or match() function to include additional patterns or specific workstation names based on your organisation's threat models.

• Additional Fields: Extend the query with fields like SourceIP or DestinationIP for more in-depth analysis.

• Enrichment: Integrate with threat intelligence feeds to correlate suspicious workstation names or IPs.

Authlogs

Detecting authentication events from suspicious workstation names, assuming the use of a custom authentication-related index (e.g., index=authlogs) and avoiding wineventlog. This query works with generic authentication data sources.

Note: The fields in your Splunk logs may differ slightly; for example, AccountName may be displayed as Account_Name.

index=authlogs sourcetype=authentication 
| eval WorkstationName=coalesce(device_name, host_name, client_hostname)  // Normalize workstation name field
| search WorkstationName IN ("TEMP*", "DESKTOP-*", "UNKNOWN*", "WORKSTATION-*")  // Match suspicious patterns
| eval Suspicious=if(match(WorkstationName, "^(TEMP|DESKTOP|UNKNOWN|WORKSTATION-).*"), "Yes", "No")
| stats count AS TotalAttempts, 
        count(eval(status="failure")) AS FailedAttempts, 
        count(eval(status="success")) AS SuccessfulAttempts, 
        dc(user) AS UniqueUsers 
        by WorkstationName, user, logon_type, Suspicious
| eval LogonTypeDescription=case(
        logon_type=="interactive", "Interactive",
        logon_type=="network", "Network",
        logon_type=="remote", "Remote Interactive",
        logon_type=="service", "Service",
        logon_type=="batch", "Batch",
        true(), "Unknown"
    )
| where Suspicious="Yes"
| sort - TotalAttempts
| table WorkstationName, user, LogonTypeDescription, TotalAttempts, SuccessfulAttempts, FailedAttempts, UniqueUsers

Key Details:

1. Custom Index and Sourcetype:

   • Replace index=authlogs and sourcetype=authentication with your specific index and sourcetype for authentication data.

2. Normalisation:

   • Uses coalesce() to extract workstation names from possible fields (device_name, host_name, or client_hostname).

3. Suspicious Patterns:

   • Matches workstation names using both the IN clause for patterns and a regex (match() function).

4. Stats Aggregation:

   • Aggregates data to summarise:

     • TotalAttempts: Total logon attempts.

     • FailedAttempts: Failed logon attempts.

     • SuccessfulAttempts: Successful logon attempts.

     • UniqueUsers: Count of distinct users involved.

5. Logon Type Mapping:

   • Converts logon_type into human-readable descriptions.

6. Filtering and Sorting:

   • Filters suspicious workstation names with where Suspicious="Yes".

   • Sorts results by the number of total attempts.

7. Output:

   • Presents key fields: WorkstationName, user, LogonTypeDescription, TotalAttempts, SuccessfulAttempts, FailedAttempts, and UniqueUsers.

Customisation:

• Field Names: Update field names to match your specific dataset (e.g., device_name, status, user, etc.).

• Patterns: Adjust workstation name patterns based on your environment or known threat actor behaviour.

• Time Filtering: Add earliest and latest time filters for specific date ranges, e.g., earliest=-24h.