Account Security

Real-Time Account Takeover Detection

Correlate login anomalies, device fingerprint changes, and behavioral signals across event streams using SQL. RisingWave flags account takeover attempts within milliseconds, before fraudulent transactions or data access completes.

Start Free Fraud Detection

Sub-Second

ATO Detection

Login anomalies, device changes, and behavioral signals correlated across event streams and flagged within milliseconds of the first indicator

Multi-Signal

ATO Signals

Correlate new device, impossible travel, credential reset, and high-value action signals in a single SQL detection rule per account

SQL

Detection Logic

Express complex ATO patterns as SQL window aggregations and stream-table joins without custom fraud model code or ML infrastructure

PostgreSQL

Integration

Feed ATO flags to step-up authentication systems, fraud case management, and block lists via standard PostgreSQL protocol

Why Real-Time ATO Detection

Why does account takeover detection require real-time event correlation?

Account takeover attacks succeed because they complete in minutes. An attacker logs in with stolen credentials, resets the recovery email, and initiates a high-value transaction within a two-minute window. Batch fraud reviews check accounts hours after the fact. Real-time event correlation evaluates ATO signals across login, profile change, and transaction streams simultaneously as events arrive.

Factor	Batch Review	RisingWave
Detection Window	Hours after the attack completes	Milliseconds during the attack sequence
Signal Correlation	Single-event rules on indexed logs	Multi-stream SQL correlation
Step-Up Trigger	Next login after batch review	Immediate during the attack session
False Positive Control	Threshold tuning in rule engine	SQL window deduplication

→Detect credential stuffing and ATO sequences during the attack session, not after the account is drained
→Correlate new device registration, password reset, and transaction initiation as a combined SQL rule
→Trigger step-up authentication or account lock within the same session where ATO signals appear
→Reduce false positives with SQL window deduplication that accounts for legitimate device and location changes

ATO Signal Detection

What account takeover signals does streaming correlation detect?

ATO attacks follow recognizable multi-event sequences across authentication, profile, and transaction streams. Streaming SQL correlation detects these sequences as they unfold, combining signals that appear innocuous in isolation but indicate compromise when correlated within a session window.

Impossible Travel Detection

Flag logins from geographically impossible locations by joining the live authentication stream against recent login history, calculating the physical distance and time delta between consecutive logins to surface logins that could not represent legitimate travel

New Device with High-Value Action

Detect account takeover sequences where a new device fingerprint is followed by a credential change or high-value transaction within a configurable time window, using SQL session window aggregations over correlated authentication and transaction streams

Credential Stuffing Campaigns

Identify credential stuffing attacks at the campaign level by correlating failed login attempts across many accounts from shared IP ranges, ASNs, or device fingerprint clusters within rolling time windows using SQL GROUP BY aggregations

Session Anomaly Scoring

Score live sessions by combining velocity, device, location, and behavioral signals from multiple event streams into a composite SQL expression, flagging sessions that exceed a risk threshold for step-up authentication challenges

How It Works

How does RisingWave correlate ATO signals across event streams?

RisingWave ingests authentication, profile change, and transaction event streams from Kafka and maintains materialized views of account-level signal state. Each new event updates the relevant window aggregations incrementally. When the combined signal state for an account crosses an ATO detection threshold, the materialized view surfaces the account for immediate action.

→Create Kafka sources in RisingWave for authentication events, profile change events, and transaction events using SQL CREATE SOURCE statements
→Maintain account-level state as SQL materialized views: recent login locations, device fingerprint history, and transaction velocity per account
→Define ATO detection rules as SQL expressions that combine account state signals, for example new device AND password reset AND transaction within 10 minutes
→Join event streams against account risk context tables via stream-table joins, incorporating account age, historical fraud flags, and step-up authentication status
→Query the ATO detection materialized view from your authentication service to trigger step-up challenges, account locks, or fraud case creation in real time

Frequently Asked Questions

How do I detect account takeover in real time using streaming data?

Ingest authentication, profile change, and transaction event streams from Kafka into RisingWave. Define SQL materialized views that maintain per-account state: recent login locations, registered devices, and transaction patterns. Write ATO detection rules as SQL expressions that combine these signals within session time windows. Query the detection view from your authentication service to trigger step-up challenges or locks within the attack session.

How does streaming ATO detection compare to rule-based fraud engines?

Rule-based fraud engines evaluate single-event rules against individual transactions. ATO attacks require correlating multiple events across authentication and transaction streams within a session window, which single-event rules cannot express. RisingWave SQL materialized views maintain session-level account state across all event streams simultaneously, enabling multi-event ATO detection that rule engines require complex CEP configuration to approximate.

Can I detect ATO at the campaign level, not just per account?

How do I minimize false positives for legitimate device and location changes?

Add SQL window conditions that account for expected change patterns. Suppress ATO flags for accounts that recently self-initiated a device registration from a trusted session. Apply velocity thresholds: a single new device login is low risk, but a new device combined with a password reset and a high-value transaction within five minutes is high risk. Tune window widths and signal combinations in SQL without redeploying the detection pipeline.

Flag account takeovers during the attack, not after

Correlate login, device, and transaction signals in SQL and trigger step-up authentication within the same session where ATO indicators appear.

Start Free