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detection_model.md

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Detection Model

Cerberus CT uses multiple detectors and enrichment layers. Each detector produces a Finding. Findings for the same domain can be grouped into a DomainAlert.

Finding fields

domain      normalized domain
detector    detector name
severity    info, low, medium, high, or critical
score       numeric score from 0 to 100
reasons     human-readable reasons
evidence    structured evidence values

Detectors

Keyword detector

Looks for configured suspicious words such as:

login
verify
secure
account
reset
support
wallet
password
billing
invoice
auth
signin

Brand detector

Looks for configured brand names in observed domains.

Typosquat detector

Compares domain labels and useful hyphenated tokens to configured official domains.

Example:

paypa1-login.com → paypal.com

Homoglyph detector

Flags IDN/punycode and non-ASCII risk.

Example:

xn--example.com

Composition detector

Scores suspicious domain construction patterns that are weak alone but meaningful together:

paypal-secure-login.com
paypa1-login.com
login.account-reset.example

The detector emits explainable evidence for combinations such as protected-name plus security-action terms, digit substitutions, excessive hyphenation, deep label nesting, and high-abuse campaign TLDs.

DNS enrichment

When enabled, Cerberus adds DNS evidence:

dns.resolved
dns.ip
dns.cname
dns.error

Takeover candidate detection

When enabled, Cerberus checks CNAMEs against known third-party provider fingerprints. It only reports candidate risk conservatively and avoids flagging resolved CNAME targets as takeover candidates.

The fingerprint dataset is checked in under crates/cerberus-core/data/ and can be refreshed from the upstream community source with:

./scripts/update_takeover_fingerprints.ps1

Rule quality controls

rules.min_score suppresses low-score alerts.

rules.allowlist_suffixes suppresses trusted suffixes and helps reduce noise from known infrastructure.