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Laya-Phishield describes a local phishing detector that combines deterministic checks on email headers and URLs with a scoring model whose feature contributions analysts can inspect. Its author reports an AUC of 0.953 on a 183-email benchmark, but that result is not an independently verified guarantee of performance on other mail. The author also explicitly says the tool is not a replacement for a production secure email gateway.
How the detector combines email checks and a scoring model
The project article describes a two-layer approach. A deterministic pre-pass checks headers and URLs for indicators such as mismatched reply-to addresses, punycode or IP-literal links, homoglyphs in domains, and SPF or DKIM failures. A second layer evaluates eight narrow yes-or-no signals, including credential requests, urgency, brand impersonation, and external-link risk. A logistic-regression head combines the signals into a risk score, with feature contributions exposed to help an analyst see why a message received that score. These are the architecture and interpretability claims made by the project author, not findings from an independent evaluation. Project article.
What the author’s benchmark measured
The project author reports an evaluation on 183 emails: 105 legitimate and 78 phishing. The article says the set used a temporal split and was deduplicated with MinHash/LSH, drawing from the Nazario phishing corpus and Enron. The reported figures below are the author’s results on that described sample; independent replication, dataset versions, licensing, and detailed split artifacts are not established here. Benchmark results.
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|---|---|---|---|
| Keyword rules | 0.594 | 1.000 | 0.051 |
| Forced choice | 0.944 | 0.902 | 0.590 |
| Composite | 0.953 | 0.913 | 0.808 |
AUC summarizes how well a method ranks positive cases above negative ones across thresholds; it does not specify the number of false alarms at the threshold an organization will use. Precision and recall in the table are threshold-dependent and, as reported, apply at 0.5. The author’s composite result has higher reported recall than forced choice at that threshold, while their AUCs are close. None of these values establishes how either method will perform on a different organization’s mail or a different phishing mix.
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The author also reports a comparison at a common 95% recall target: forced choice has a false-positive rate of 0.124, versus 0.210 for the composite. At that operating point, the forced-choice variant raises fewer false alarms in the reported benchmark, while the composite has the higher recall in the table at threshold 0.5. The results illustrate why there is no universally best choice: a team may prioritize catching more phishing or reducing interruptions caused by legitimate messages being flagged. These are author-reported benchmark comparisons, not universal rankings. Variant comparison.
What “$0 per 1,000 emails” does—and does not—mean
The author frames local decisions as costing $0 per 1,000 emails. That describes the claimed per-email cost of making local decisions, not total ownership cost: it does not include a reader’s hardware, deployment, maintenance, or analyst time. The benchmark pipeline is also described as calling a hosted model to generate and judge baselines, while local decisions themselves do not call that model. The benchmark as a whole should therefore not be characterized as entirely local or cost-free. Benchmark and cost description.
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Installation and ways the project says to run it
The project article gives the following installation command and workflow examples. These are the author’s instructions; package availability, current versions, compatibility, and successful installation have not been independently checked. Project article.
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Install the package:
pip install laya-phishield. -
Scan an email file in the command-line workflow with
laya-scanand an.emlfile. -
For an API workflow, the article says to serve the API with
laya-serve. -
The article also mentions a Streamlit demo.
Why it is not a secure email gateway replacement
The project article’s own disclaimer is: “this is not a replacement for a production secure email gateway. It is an explainable, local signal layer for analysts and researchers.” Treat the tool accordingly: the described risk score and feature contributions may help an analyst inspect signals, but the benchmark does not establish production readiness, protection coverage, or performance across real-world mail systems. Project disclaimer.
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