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Detect unauthorized data transfers using timeseries anomaly (ASIM Web Session)

Description

'This query utilizes built-in KQL anomaly detection algorithms to identify anomalous data transfers to public networks. It detects significant deviations from a baseline pattern, allowing the detection of sudden increases in data transferred to unknown public networks, which may indicate data exfiltration attempts. Investigating such anomalies is crucial. The score indicates the degree to which the data transfer deviates from the baseline value. A higher score indicates a greater deviation. The query's output provides an aggregated summary view of the traffic observed in the flagged anomaly hour, including unique combinations of source IP addresses, destination IP addresses, and port bytes sent. It may be necessary to run queries for individual source IP addresses from the provided 'SourceIPlist' to identify any suspicious activity that warrants further investigation'
Rule type
Scheduled
Version
1.0.2
Declared status
Available
Query frequency
1d
Query period
14d
Trigger
gt 0

Declared MITRE coverage

KQL query

Original query, unchanged.

let startTime = 14d;
let endTime = 1d;
let timeframe = 1h;
let scorethreshold = 5;
let bytessentperhourthreshold = 10;
// calculate avg. eps(events per second)
let eps = materialize(_Im_WebSession(starttime=ago(1d))
    | project TimeGenerated
    | summarize AvgPerSec = count() / 3600 by bin(TimeGenerated, 1h)
    | summarize round(avg(AvgPerSec))
    );
let summarizationexist  = (
    union isfuzzy=true 
        (
        WebSessionEssentialsCustomParser
        | where Type in ("WebSession_Summarized_SrcIP_CL", "WebSession_Summarized_SrcIPV1_CL")
        | where EventTime_t > ago(1d) 
        | project v = int(2)
        ),
        (
        print int(1) 
        | project v = print_0
        )
    | summarize maxv = max(v)
    | extend sumexist = (maxv > 1)
    );
let TimeSeriesData = union isfuzzy=true 
        (
        (datatable(exists: int, sumexist: bool)[1, false]
        | where toscalar(eps) > 1000
        | join (summarizationexist) on sumexist)
        | join (
            _Im_WebSession(starttime=ago(2d), endtime=now())
            | project DstIpAddr, SrcBytes, TimeGenerated, EventProduct
            | where isnotempty(DstIpAddr)
                and not(ipv4_is_private(DstIpAddr))
                and isnotempty(SrcBytes)
            | summarize SrcBytesSum=tolong(sum(SrcBytes)) by EventProduct, bin(TimeGenerated, 1h)
            | extend EventTime = TimeGenerated, exists=int(1)
            )
            on exists
        | project-away exists*, maxv, sum*
        ),
        (
        (datatable(exists: int, sumexist: bool)[1, false]
        | where toscalar(eps) between (501 .. 1000)
        | join (summarizationexist) on sumexist)
        | join (
            _Im_WebSession(starttime=ago(3d), endtime=now())
            | project DstIpAddr, SrcBytes, TimeGenerated, EventProduct
            | where isnotempty(DstIpAddr)
                and not(ipv4_is_private(DstIpAddr))
                and isnotempty(SrcBytes)
            | summarize SrcBytesSum=tolong(sum(SrcBytes)) by EventProduct, bin(TimeGenerated, 1h)
            | extend EventTime = TimeGenerated, exists=int(1)
            )
            on exists
        | project-away exists*, maxv, sum*
        ),
        (
        (datatable(exists: int, sumexist: bool)[1, false]
        | where toscalar(eps) <= 500
        | join (summarizationexist) on sumexist)
        | join (
            _Im_WebSession(starttime=ago(4d), endtime=now())
            | project DstIpAddr, SrcBytes, TimeGenerated, EventProduct
            | where isnotempty(DstIpAddr)
                and not(ipv4_is_private(DstIpAddr))
                and isnotempty(SrcBytes)
            | summarize SrcBytesSum=tolong(sum(SrcBytes)) by EventProduct, bin(TimeGenerated, 1h)
            | extend EventTime = TimeGenerated, exists=int(1)
            )
            on exists
        | project-away exists*, maxv, sum*
        ),
        (
        WebSessionEssentialsCustomParser
        | where Type in ("WebSession_Summarized_SrcIP_CL", "WebSession_Summarized_SrcIPV1_CL")
        | where EventTime_t between (ago(startTime) .. now())
        | where isnotempty(SrcBytes_d) and not(DstIPIsPrivate_b)
        | project
            SrcBytesSum=tolong(SrcBytes_d),
            EventTime=EventTime_t,
            EventProduct = EventProduct_s
        )
    | make-series TotalBytesSent = sum(SrcBytesSum) on EventTime from startofday(ago(startTime)) to startofday(now()) step timeframe by EventProduct;
// TimeSeriesData block ends here
//Take only anomalies in TimeSeriesData
let TimeSeriesAnomalies = materialize(TimeSeriesData
    | extend (anomalies, score, baseline) = series_decompose_anomalies(TotalBytesSent, scorethreshold, -1, 'linefit')
    | mv-expand
        TotalBytesSent to typeof(long),
        EventTime to typeof(datetime),
        anomalies to typeof(double),
        score to typeof(double),
        baseline to typeof(long)
    | where anomalies > 0 and baseline > 0
    | extend AnomalyHour = EventTime
    | extend
        TotalBytesSentinMBperHour = round(((TotalBytesSent / 1024) / 1024), 2),
        BaselineBytesSentinMBperHour = round(((baseline / 1024) / 1024), 2),
        score = round(score, 2)
    | project
        EventProduct,
        AnomalyHour,
        TotalBytesSentinMBperHour,
        BaselineBytesSentinMBperHour,
        anomalies,
        score
    | where AnomalyHour between (startofday(ago(endTime)) .. startofday(now())) // Get TimeSeriesAnomalies in previous day
        );
// TimeSeriesAlerts block end here
let AnomalyHours = materialize (TimeSeriesAnomalies
    | project AnomalyHour);
//Previous day aggregated per hour
let PreviousDayLogs = 
    _Im_WebSession(starttime=startofday(ago(endTime)), endtime=startofday(now()))
    | where isnotempty(DstIpAddr) and isnotempty(SrcIpAddr) and isnotempty(SrcBytes)
    | where not(ipv4_is_private(DstIpAddr))
    | project
        TimeGenerated,
        DstIpAddr,
        SrcIpAddr,
        SrcBytes,
        DstBytes,
        DstPortNumber,
        EventProduct
    | extend DateHour = bin(TimeGenerated, timeframe) // create a new column and round to hour
    | where DateHour in (AnomalyHours) // Filter dataset to include only anomaly AnomalyHours
    | extend
        SentBytesinMB = ((SrcBytes / 1024) / 1024),
        ReceivedBytesinMB = ((DstBytes / 1024) / 1024)
    | summarize
        HourlyCount = count(),
        TimeGeneratedMax = arg_max(TimeGenerated, *),
        DestinationIPList = make_set(DstIpAddr, 100),
        DestinationPortList = make_set(DstPortNumber, 100),
        SentBytesinMB = tolong(sum(SentBytesinMB)),
        ReceivedBytesinMB = tolong(sum(ReceivedBytesinMB))
        by SrcIpAddr, EventProduct, TimeGeneratedHour = bin(TimeGenerated, timeframe)
    | where SentBytesinMB > bytessentperhourthreshold
    | sort by TimeGeneratedHour asc, SentBytesinMB desc
    | extend Rank=row_number(1, prev(TimeGeneratedHour) != TimeGeneratedHour) // Ranking the dataset per Hourly Partition
    | where Rank <= 10  // Selecting Top 10 records with Highest BytesSent in each Hour
    | project
        EventProduct,
        TimeGeneratedHour,
        TimeGeneratedMax,
        SrcIpAddr,
        DestinationIPList,
        DestinationPortList,
        SentBytesinMB,
        ReceivedBytesinMB,
        Rank,
        HourlyCount;
// PreviousDayLogs block ends here
TimeSeriesAnomalies
| join kind = inner (PreviousDayLogs
    | extend AnomalyHour = TimeGeneratedHour)
    on EventProduct, AnomalyHour
| sort by score desc
| project
    EventProduct,
    AnomalyHour,
    TimeGeneratedMax,
    SrcIpAddr,
    DestinationIPList,
    DestinationPortList,
    SentBytesinMB,
    ReceivedBytesinMB,
    TotalBytesSentinMBperHour,
    BaselineBytesSentinMBperHour,
    score,
    anomalies,
    HourlyCount
| summarize
    EventCount = sum(HourlyCount),
    startTimeUtc = min(TimeGeneratedMax),
    EndTimeUtc = max(TimeGeneratedMax),
    SentBytesinMB = sum(SentBytesinMB),
    ReceivedBytesinMB = sum(ReceivedBytesinMB),
    SourceIP = take_any(SrcIpAddr),
    SourceIPList = make_set(SrcIpAddr, 10),
    DestinationIPList = make_set(DestinationIPList, 100),
    DestinationPortList = make_set(DestinationPortList, 100)
    by
    AnomalyHour,
    TotalBytesSentinMBperHour,
    BaselineBytesSentinMBperHour,
    score,
    anomalies,
    EventProduct
| project
    EventProduct,
    AnomalyHour,
    startTimeUtc,
    EndTimeUtc,
    SourceIP,
    SourceIPList,
    DestinationIPList,
    DestinationPortList,
    SentBytesinMB,
    ReceivedBytesinMB,
    TotalBytesSentinMBperHour,
    BaselineBytesSentinMBperHour,
    anomalies,
    score,
    EventCount

Declared entities

IP

Related content

Links established from declared identifiers and solution manifests.

Source provenance

GitHub

Displayed values come from files in Azure/Azure-Sentinel. They describe the published template, not your workspace configuration.

Source identifier
5965d3e7-8ed0-477c-9b42-e75d9237fab0
Additional source files 2Solutions/Web Session Essentials/Analytic Rules/DataExfiltrationTimeSeriesAnomaly.yamlsource ↗Solutions/Web Session Essentials/Data/Solution_Web Session Essentials.jsonsolution-membership ↗
GSTEP / CATALOG TRACKING

Added to catalog : 16 Sept 2026 · 05:49 UTC
Last change observed : 16 Sept 2026 · 05:49 UTC

GSTEP sync dates, separate from the source content’s publication dates.