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Time series anomaly detection for total volume of traffic

Description

'Identifies anamalous spikes in network traffic logs as compared to baseline or normal historical patterns. The query leverages a KQL built-in anomaly detection algorithm to find large deviations from baseline patterns. Sudden increases in network traffic volume may be an indication of data exfiltration attempts and should be investigated. The higher the score, the further it is from the baseline value. The output is aggregated to provide summary view of unique source IP to destination IP address and port traffic observed in the flagged anomaly hour. The source IP addresses which were sending less than percentotalthreshold of the total traffic have been exluded whose value can be adjusted as needed . You may have to run queries for individual source IP addresses from SourceIPlist to determine if anything looks suspicious'
Type de règle
Scheduled
Version
1.0.4
Fréquence
1d
Période analysée
14d
Déclenchement
gt 3

Couverture MITRE déclarée

Sources déclarées

Métadonnées du fichier source. Aucune dépendance déduite du KQL.

Connecteurs

Types de données

Requête KQL

Requête originale, sans modification.

let starttime = 14d;
let endtime = 1d;
let timeframe = 1h;
let scorethreshold = 5;
let percentotalthreshold = 50;
let TimeSeriesData = CommonSecurityLog
| where isnotempty(DestinationIP) and isnotempty(SourceIP)
| where TimeGenerated between (startofday(ago(starttime))..startofday(ago(endtime)))
| project TimeGenerated,SourceIP, DestinationIP, DeviceVendor
| make-series Total=count() on TimeGenerated from startofday(ago(starttime)) to startofday(ago(endtime)) step timeframe by DeviceVendor;
// Filtering specific records associated with spikes as outliers
let TimeSeriesAlerts=materialize(TimeSeriesData
| extend (anomalies, score, baseline) = series_decompose_anomalies(Total, scorethreshold, -1, 'linefit')
| mv-expand Total to typeof(double), TimeGenerated to typeof(datetime), anomalies to typeof(double),score to typeof(double), baseline to typeof(long)
| where anomalies > 0 | extend score = round(score,2), AnomalyHour = TimeGenerated
| project DeviceVendor,AnomalyHour, TimeGenerated, Total, baseline, anomalies, score);
let AnomalyHours = materialize(TimeSeriesAlerts  | where TimeGenerated > ago(2d) | project TimeGenerated);
// Join anomalies with Base Data to popalate associated records for investigation - Results sorted by score in descending order
TimeSeriesAlerts
| where TimeGenerated > ago(2d)
| join (
    CommonSecurityLog
| where isnotempty(DestinationIP) and isnotempty(SourceIP)
| where TimeGenerated > ago(2d)
| extend DateHour = bin(TimeGenerated, 1h) // create a new column and round to hour
| where DateHour in ((AnomalyHours)) //filter the dataset to only selected anomaly hours
| summarize HourlyCount = count(), TimeGeneratedMax = arg_max(TimeGenerated, *), DestinationIPlist = make_set(DestinationIP, 100), DestinationPortlist = make_set(DestinationPort, 100) by DeviceVendor, SourceIP, TimeGeneratedHour= bin(TimeGenerated, 1h)
| extend AnomalyHour = TimeGeneratedHour
) on AnomalyHour, DeviceVendor
| extend PercentTotal = round((HourlyCount / Total) * 100, 3)
| where PercentTotal > percentotalthreshold
| project DeviceVendor , AnomalyHour, TimeGeneratedMax, SourceIP, DestinationIPlist, DestinationPortlist, HourlyCount, PercentTotal, Total, baseline, score, anomalies
| summarize HourlyCount=sum(HourlyCount), StartTimeUtc=min(TimeGeneratedMax), EndTimeUtc=max(TimeGeneratedMax), SourceIPlist = make_set(SourceIP, 100), SourceIPMax= arg_max(SourceIP, *), DestinationIPlist = make_set(DestinationIPlist, 100), DestinationPortlist = make_set(DestinationPortlist, 100) by DeviceVendor , AnomalyHour, Total, baseline, score, anomalies
| project DeviceVendor , AnomalyHour, EndTimeUtc, SourceIPMax ,SourceIPlist, DestinationIPlist, DestinationPortlist, HourlyCount, Total, baseline, score, anomalies

Entités déclarées

IP

Contenus associés

Liens établis à partir des identifiants déclarés et des manifests des solutions.

Traçabilité de la source

GitHub

Les valeurs affichées proviennent des fichiers du dépôt Azure/Azure-Sentinel. Elles décrivent le modèle publié, pas la configuration de votre workspace.

Identifiant source
06a9b845-6a95-4432-a78b-83919b28c375
Autres fichiers source 1Detections/CommonSecurityLog/TimeSeriesAnomaly-MultiVendor_NetworkTraffic.yamlsource ↗
GSTEP / SUIVI DU CATALOGUE

Ajouté au catalogue : 16 sept. 2026 · 05:49 UTC
Dernier changement observé : 16 sept. 2026 · 05:49 UTC

Dates de synchronisation GSTEP, distinctes des dates de publication du contenu source.