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Detection methodology

Click Fraud Detection Methodology and Risk Scoring

Clickronix Detection Methodology

Understand the categories of network, click, device, session, campaign, and historical signals used to calculate traffic-risk scores.

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Risk model

Multiple indicators contribute to an explainable result.

Clickronix does not treat one IP address, one short session, or one repeat click as conclusive proof. Related signals are evaluated together.

  • IP reputation and network characteristics
  • Click frequency, speed, and repeated behavior
  • Browser, operating system, device, and user-agent context
  • Session quality, historical patterns, and authorized campaign context
    • 85
    Example high-risk result from several contributing indicators
    15
    Residual uncertainty kept visible for user judgment
    Signal architecture

    Six evidence groups contribute to one explainable risk result.

    Clickronix combines technical and behavioral context rather than treating one data point as proof. Each group remains visible in the event record so teams can understand the result.

    001 IP intelligence Location, ISP, ASN, VPN, proxy, hosting, and datacenter context.
    002 Click patterns Repeat frequency, burst speed, session spacing, and historical activity.
    003 Device context Browser, OS, device category, user agent, and unexpected changes.
    C Risk model
    004 Engagement Session duration, navigation, bounce behavior, and configured outcomes.
    005 Campaign context Authorized account, campaign, ad group, keyword, and click information.
    006 Classification Allow, monitor, flag, investigate, or use an enabled exclusion workflow.
    Important limitation

    Risk scores support decisions; they do not replace judgment.

    Legitimate users may share IP addresses, use mobile networks, travel, use privacy tools, or revisit an offer several times. Clickronix is designed to present evidence and configurable thresholds so advertisers can decide how to handle uncertain events.

    Clickronix workflow

    Six signal groups support one explainable risk result

    Clickronix evaluates network, timing, device, session, campaign, and historical context together rather than treating one indicator as proof of invalid activity.

    Clickronix
    Traffic workflow
    01 Network context
    02 Click patterns
    03 Device signals
    04 Session behavior
    • 01 Network context
    • 02 Click patterns
    • 03 Device signals
    • 04 Session behavior
    Multi-signal detection

    Six signal groups behind an explainable risk assessment.

    Clickronix is designed to correlate independent evidence so risk scoring is understandable and reviewable.

    Network ASN / VPN / proxy
    Identity Fingerprint / device
    Velocity Repeat clicks / bursts
    Session Navigation / engagement
    Campaign GCLID / source
    History Prior events / blocks
    Clickronix Risk Engine Correlate → score → explain
    Detection model

    Risk score and confidence answer different questions.

    Risk can describe how concerning the observed pattern is; confidence can describe how strongly the available evidence supports that assessment.

    Supporting evidence

    A weak or common signal can add context without triggering action alone.

    Correlated evidence

    Independent signals that point in the same direction can increase confidence in the traffic-quality assessment.

    Action threshold

    Allow, monitor, review, challenge or block behavior should follow explicit rules and customer policy—not hidden guesswork.

    Traffic protection built for action

    Use explainable signals instead of black-box accusations.

    Clickronix detection reports show the network, click, device, session, and campaign indicators behind each risk result.

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    Frequently asked questions

    Methodology and scoring questions

    Why use more than one signal?

    Many legitimate visitors share characteristics with suspicious traffic. Combining independent evidence reduces the chance that one noisy field drives the whole decision.

    Can a rule act without a score?

    A narrowly defined high-confidence rule can be configured to act independently when its evidence is strong enough and the customer chooses that policy. Other signals are better used as score or confidence contributors.

    How should false positives be reduced?

    Use evidence thresholds, separate supporting signals from decisive triggers, review shared-network scenarios, and test rules against legitimate traffic before broad blocking.