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Explainable Phishing Detection: What the Local laya-phishield Benchmark Shows

laya-phishield describes a local, explainable phishing detector. Its author reports AUC 0.953 on a 183-email benchmark; here’s how to interpret the results and limits.
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laya-phishield describes a local phishing detector that combines deterministic checks on email headers and links with a scored set of phishing signals. Its author reports an AUC of 0.953 on a 183-email benchmark and frames local decisions as costing $0 per 1,000 emails. Those figures describe the author’s specific benchmark and cost framing—not independently verified performance or a guarantee for other mailboxes.

How laya-phishield says it detects phishing

The project article describes a two-layer workflow. A deterministic pre-pass examines headers and URLs for suspicious indicators; a second layer scores a set of narrow yes-or-no signals. A logistic-regression head combines those signals into a risk score, with feature contributions exposed so an analyst can inspect why a message was marked risky. This is the project author’s description of the design, not an independent technical audit. Project article

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Deterministic checks

The first layer looks for indicators such as a mismatch between the sender and reply-to address, punycode or IP-literal links, domain homoglyphs, and SPF or DKIM failures. These are signals to assess, not proof on their own that a message is malicious.

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Scored signals

The second layer uses eight yes-or-no signals, including requests for credentials, urgency pressure, brand impersonation, and risk from external links. The logistic-regression head combines them. Showing feature contributions is intended to make the score inspectable; it does not by itself establish that the score is accurate in every environment.

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What the reported AUC and cost mean

The project author reports an AUC of 0.953 for the composite method in 2026. The benchmark contained 183 emails—105 legitimate and 78 phishing—drawn from the Nazario phishing corpus and Enron. The article says the data used a temporal split and were deduplicated with MinHash/LSH. These details describe the author’s benchmark; independent replication, dataset versions and licensing, and detailed split artifacts are not established here. Benchmark article

AUC summarizes how well a scoring method ranks positive examples above negative ones across thresholds; it is not the same as the fraction of emails correctly classified at one operating threshold. The author also reports recall of 0.808 at threshold 0.5. Neither figure establishes how the detector will perform on a different organization’s mail, new attack patterns, or a different mix of legitimate and phishing messages.

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The author’s “$0 per 1,000 emails” framing refers to local decisions. The same article says its benchmark pipeline—not those local decisions—calls a hosted model to generate and judge baselines. The figure is not a verified total cost of ownership: it excludes a reader’s hardware and operational costs, among other possible expenses. Benchmark article

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How the compared variants trade off missed phishing and false alarms

The author’s comparison reports the following results on the described benchmark. Precision and recall are at threshold 0.5 where applicable; the common-recall comparison is reported separately.

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Method AUC Precision at threshold 0.5 Recall at threshold 0.5 False-positive rate at 95% recall
Keyword rules 0.594 1.000 0.051 not stated in the project article
Forced choice 0.944 0.902 0.590 0.124
Composite 0.953 0.913 0.808 0.210

All figures in the table are reported by the project author for the small, described benchmark; they are not independent measurements. The author’s 95%-recall comparison shows why the highest AUC is not automatically the best operational choice: at that recall target, the forced-choice variant has a lower reported false-positive rate than the composite, 0.124 versus 0.210. A workflow that prioritizes catching more phishing may tolerate more false alarms; one where analyst capacity is scarce may prefer fewer false alarms, even if more phishing is missed. Benchmark article Variant comparison

The project article says the composite includes deterministic header and URL checks. It does not establish the same checks for the forced-choice variant, so the variants should not be treated as interchangeable configurations with only a different threshold. Choose an operating point using the costs of false alarms and missed phishing in the actual workflow, then validate it on representative mail before relying on it.

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Installation and example commands in the project article

The project article gives these commands and workflow examples. They are the author’s instructions; current package availability, versions, compatibility, and successful installation have not been independently confirmed.

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  1. Install the package: pip install laya-phishield

  2. Scan an email file in .eml format with laya-scan. The article does not specify a complete command line in the material described here.

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  3. Serve an API with laya-serve; the project article also mentions a Streamlit demo.

Consult the project article for its exact invocation details rather than assuming flags or deployment settings. Project article

What the project is—and is not—positioned to do

The author explicitly states: “this is not a replacement for a production secure email gateway. It is an explainable, local signal layer for analysts and researchers.” That distinction matters: the article presents a tool and a limited benchmark, not evidence that it supplies the full protections or operational controls expected of a production gateway. Project disclaimer

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The most defensible takeaway is narrow: the project describes a locally run scoring layer with inspectable signals, and its author reports promising results on a small benchmark. The reported metrics can help readers understand the author’s experiment, but they should not be generalized into a performance guarantee or used alone to justify production deployment.

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