What Password Spraying Looks Like in Raw Network Telemetry

Password spraying is usually described in terms of failed logins and account lockouts. But before a SIEM rule fires or a helpdesk ticket is opened, the network already shows you what’s happening.

If you follow authentication flows in raw telemetry, password spraying has a distinct shape. It appears in timing, peer relationships, service usage, and account distribution. When behavioral anomaly detection is applied to that telemetry, the pattern becomes visible without relying on static thresholds or signature matching.

The Mechanics on the Network

Password spraying differs from traditional brute force attacks. Instead of trying many passwords against one account, an attacker tests one common password across many accounts. Their objective is to avoid lockouts and stay under per-account alert thresholds.

In environments using Kerberos or NTLM, the pattern often centers on domain controllers. A single source host begins generating authentication flows toward the same authentication service within a compressed time window. Each flow is technically valid at the protocol level; what changes is the distribution of targeted accounts.

In raw network telemetry, this activity typically shows up as:

• Repeated authentication flows from one source to a domain controller
• Short-lived connections with consistent destination ports
• Many distinct user accounts referenced in close succession
• A burst of activity contained within minutes

Individually, each flow appears routine. In aggregate, the pattern diverges from that host’s normal communication profile.

Unlike brute force attacks, which concentrate on one account with high repetition, password spraying distributes attempts broadly. The abnormality is not in the volume per account, but in the sudden expansion of account targets tied to a single source.

Why Static Thresholds Miss It

Most static detection logic looks for high failure counts tied to one account or one source. Password spraying is engineered to avoid exactly that.

Each account may see only one or two attempts. From the perspective of per-user thresholds, that can appear normal. What changes is the host’s behavioral profile.

Behavioral anomaly detection approaches the problem differently. Instead of counting failures in isolation, it baselines normal authentication behavior for each asset and flags meaningful deviations in frequency, timing, and peer relationships.

For example, a workstation that normally authenticates as one or two users per day may suddenly reference dozens of accounts in a ten-minute window. That deviation stands out even if the total traffic volume remains modest.

With machine learning—for instance, with Plixer ML Engine—you can apply unsupervised learning directly to flow-derived features to learn normal communication patterns per asset or service, then flags meaningful deviations in frequency and peer activity. In practice, password spraying appears as a behavioral shift in authentication patterns rather than a simple spike in failures.

Password spraying is characterized by anomalous login activity across different user accounts within a short window. At the network layer, that translates to one source communicating with authentication services while referencing an unusually broad set of accounts in a compressed timeframe.

A Walkthrough in Telemetry

Imagine reviewing flow data from a domain controller during a fifteen-minute window.

You filter for Kerberos traffic and sort by top talkers. One internal IP appears higher than expected, despite not typically acting as an authentication hub. When you pivot into that source, you observe consistent connections to the same domain controller, evenly spaced, each short in duration.

When you overlay a timeline, the behavior tightens. At 02:13 AM, authentication flows begin to increase. Over the next eight minutes, the host references dozens of distinct accounts. After 02:21 AM, activity returns to baseline.

There is no single explosive metric. Instead, you see a compressed burst, broad account distribution, and deviation from historical norms.

From a behavioral standpoint, that deviation can include:

• A sudden increase in unique accounts referenced by one host
• Authentication bursts outside normal working hours
• New communication patterns between an asset and identity infrastructure

The anomaly isn’t just in volume, but in who’s communicating, to which service, how often, and within what time boundary.

Flow-First Visibility Changes the Investigation

Authentication logs provide details about success or failure. Flow telemetry provides independent evidence of who communicated with which service, when, and how often. Because flow data is lightweight, it can be retained for extended periods, allowing teams to compare current activity to weeks or months of historical behavior.

That historical context matters.

If an anomalous authentication burst is followed by new east-west connections, remote access activity, or unusual SMB traffic, the sequence becomes visible in the same dataset. Password spraying often precedes lateral movement; seeing the initial behavioral shift early can shorten the investigative path.

Behavioral anomaly detection does not generate noise by counting every failed login. Instead, it surfaces the host whose authentication behavior changed in a meaningful way. Analysts can then pivot directly into:

• The time window of deviation
• The authentication service involved
• The set of accounts referenced
• Subsequent peer communications

Because the detection is grounded in observable network traffic, it is explainable. You can show the exact moment the pattern changed and trace the flows that define it.

Seeing the Pattern Before the Damage

Password spraying is designed to be quiet. It spreads attempts across accounts and avoids obvious thresholds. But at the network layer, its structure is difficult to hide because it has timing, direction, and distribution.

When behavioral anomaly detection is applied to authentication flows, that structure becomes visible as a deviation from baseline rather than a flood of alerts.

In a unified observability model, the network is not just transport, but evidence. And when you follow the authentication flows closely enough, password spraying stops looking subtle.

Want to see it in action? Book a Plixer One demo with one of our engineers today.