You are about to measure scam websites — a fake shop, a tech-support page that locks the browser and shows a phone number, a crypto “giveaway” that promises to double what you send, an investment platform that shows a wallet address after sign-up. Three decisions decide what your number means, and a paper that makes them silently is not reproducible:

1. Where the seed list comes from. Every discovery channel finds a different population, and two channels aimed at the same scam type can share almost nothing.
2. Whether you group sites into operators. Scam domains are cheap and disposable; a count of URLs is mostly a count of how often one crew re-registered.
3. What you count as harm. A live scam page is not a victim. The papers that say something about harm measured orders, payments, wallets or checkout visits — never the victims' total loss.

Labelling and cloaking follow from those three, and so does the ethics of talking to, buying from or reporting the people you are measuring.

This page is about scam websites and campaigns as a web phenomenon. The line with Phishing is what the victim hands over. Credentials, a seed phrase, or a signature that gives the attacker authority over the account is phishing — including wallet drainers and the “transaction-based phishing” of [1He, Bowen; Chen, Yuan; Chen, Zhuo; Hu, Xiaohui; Hu, Yufeng; Wu, Lei; Chang, Rui; Wang, Haoyu; Zhou, Yajin (2023): "TxPhishScope: Towards Detecting and Understanding Transaction-based Phishing on Ethereum", in: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. (DOI)]. Money the victim knowingly sends, believing they get goods, support or returns, belongs here. Scam accounts, comments and posts as the object of study are Platforms; this page picks a platform study up only when the off-platform scam site or its wallet is among what was measured. Phone and SMS scams, on-chain scam tokens with no website, and studies of who falls for scams are outside; they are listed under Use in publications as context, not counted.

The population of a scam study is whatever its discovery channel emits. The papers that checked this found that channels barely overlap:

• Search against malvertising, same scam type. Srinivasan et al. found tech-support scams through search results and search ads, then compared them with the malvertising-crawl data of Miramirkhani et al.: 0 of 2,768 fully qualified domains and 0 of 2,441 second-level domains in common, 92 of 1,994 IP addresses, 5 of 882 toll-free numbers [11Srinivasan, Bharat; Kountouras, Athanasios; Miramirkhani, Najmeh; Alam, Monjur; Nikiforakis, Nick; Antonakakis, Manos; Ahamad, Mustaque (2018): "Exposing Search and Advertisement Abuse Tactics and Infrastructure of Technical Support Scammers", in: Proceedings of the ACM Web Conference. (DOI)]. The search channel also surfaced a “passive” scam page — professional-looking, with a support number — that the authors argue malvertising would not carry.
• A curated list against a platform. Of 3,863 giveaway-scam domains found through Certificate Transparency, only 361 (9%) ever appeared on Twitter [8Liu, Enze; Kappos, George; Mugnier, Eric; Invernizzi, Luca; Savage, Stefan; Tao, David; Thomas, Kurt; Voelker, Geoffrey M.; Meiklejohn, Sarah (2024): "Give and Take: An End-To-End Investigation of Giveaway Scam Conversion Rates", in: Proceedings of the ACM Internet Measurement Conference. (DOI)].
• Spam feeds against each other. Across ten spam feeds, 60% of live domains and 19% of storefront-tagged domains appeared in exactly one feed; the smallest feed, human-identified spam, contributed the most unique domains [14Pitsillidis, Andreas; Kanich, Chris; Voelker, Geoffrey M.; Levchenko, Kirill; Savage, Stefan (2012): "Taster's choice: a comparative analysis of spam feeds", in: Proceedings of the ACM Internet Measurement Conference. (DOI)].

What each channel finds, and what it leans towards, in the papers of this corpus:

What to take from this into a design. Pick the channel from the victim path you care about — a shopper searching, a user served an ad, a follower on a platform — and say that your population is that channel's. If you need prevalence rather than a sample, you need a second, independent channel and the overlap between them; Leontiadis et al. ran capture–recapture estimates across query lists and got two estimates of the pharmacy population, 2,523 and 795 [16Leontiadis, Nektarios; Moore, Tyler; Christin, Nicolas (2011): "Measuring and Analyzing Search-Redirection Attacks in the Illicit Online Prescription Drug Trade", in: Proceedings of the USENIX Security Symposium. (Link)] — an honest picture of how far apart two query lists on one channel can put the total.

A scam domain is cheap and disposable; the operator is not. The papers that report how sites distribute over operators find a steep head:

The keys, in the order the literature moved through them:

• Payment identifiers are the strongest key because the operator cannot rotate them as cheaply as a domain: bank accounts (2010), acquiring banks from test purchases (2011–2012), merchant IDs from checkout pages (2025), wallet addresses (2023–2025). They are also the key that needs the most intrusive collection — see Interacting with scammers, and reporting them.
• Phone numbers and messenger contacts — tech-support scams carry them on the page [4Miramirkhani, Najmeh; Starov, Oleksii; Nikiforakis, Nick (2017): "Dial One for Scam: A Large-Scale Analysis of Technical Support Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]; comment scams move victims to WhatsApp or Telegram and the contact is the join [23Li, Xigao; Rahmati, Amir; Nikiforakis, Nick (2024): "Like, Comment, Get Scammed: Characterizing Comment Scams on Media Platforms", in: Proceedings of the Network and Distributed System Security Symposium. (Link)].
• Screenshot hashes — Vadrevu and Perdisci's difference hash with DBSCAN (eps 0.1, MinPts 3), keeping only clusters that span at least five domains, because a campaign re-hosts the same page on many domains to evade URL blocklists [19Vadrevu, Phani; Perdisci, Roberto (2019): "What You See is NOT What You Get: Discovering and Tracking Social Engineering Attack Campaigns", in: Proceedings of the ACM Internet Measurement Conference. (DOI)]. Muzammil et al. group screenshots within a Hamming distance of 8 bits.
• Analytics and tag IDs [26Starov, Oleksii; Zhou, Yuchen; Zhang, Xiao; Miramirkhani, Najmeh; Nikiforakis, Nick (2018): "Betrayed by Your Dashboard: Discovering Malicious Campaigns via Web Analytics", in: Proceedings of the ACM Web Conference. (DOI)] — cheap and effective, with a trap the authors hit: hosting providers' suspended-account error pages all share one analytics ID, so a size cap was needed to keep them from forming one giant “campaign”.
• WHOIS — useful where un-redacted; see Ownership resolution for why a registrant field is not an owner.
• IP and hosting — Wang et al. tried network features and dropped them: they “were ill-suited to differentiate SEO campaigns due to the growing popularity of shared hosting and reverse proxying infrastructure (e.g., CloudFlare)” [3Wang, David Y.; Der, Matthew F.; Karami, Mohammad; Saul, Lawrence K.; McCoy, Damon; Savage, Stefan; Voelker, Geoffrey M. (2014): "Search + Seizure: The Effectiveness of Interventions on SEO Campaigns", in: Proceedings of the ACM Internet Measurement Conference. (DOI)].

Three pitfalls the papers name about their own grouping:

• A template is not an operator. Kanich et al. found a pharmacy running the same storefront engine as a large affiliate program but with its own template, phone number, prices and bank — “not in fact associated with the GlavMed operation” [15Kanich, Chris; Weaver, Nicholas; McCoy, Damon; Halvorson, Tristan; Kreibich, Christian; Levchenko, Kirill; Paxson, Vern; Voelker, Geoffrey M.; Savage, Stefan (2011): "Show Me the Money: Characterizing Spam-advertised Revenue", in: Proceedings of the USENIX Security Symposium. (Link)].
• A sender address is not a victim. Li et al. (NDSS 2023) traced wallets that paid several scam wallets and concluded that most belong to “online exchanges (such as Coinbase), where multiple victims can share the same outgoing address” [5Li, Xigao; Yepuri, Anurag; Nikiforakis, Nick (2023): "Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)].
• Address clustering is incomplete. Across six cybercrimes including giveaway scams, “the popular multi-input clustering fails to discover addresses for 40% of groups” [28Gómez, Gibran; Liebergen, Kevin van; Caballero, Juan (2023): "Cybercrime Bitcoin Revenue Estimations: Quantifying the Impact of Methodology and Coverage", in: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. (DOI)].

In the population below, 19 of 27 papers group sites into operators or campaigns; the 8 that do not include four of the six 2022–2024 papers. A 2026 paper that reports only a domain count is doing less than the 2011 papers did.

A blocklist tells you what someone had already listed. For scam sites it is a lagging and sparse instrument, which is why using it as the label and then reporting “coverage” is circular.

Read the table as “no general-purpose list is a scam census”, not as a ranking of lists: each row uses a different population, a different list and a different lag. Muzammil et al. suspect “this low coverage in existing block-lists is due to the general nature of websites they aim to detect”. The Google Safe Browsing API has no scam threat type; scams fall under SOCIAL_ENGINEERING alongside phishing.⁵⁾ The v4 API is deprecated — “The Safe Browsing APIs (v4) are deprecated”, on Google's own v4 overview — and its end date and the v5 migration are on Google Safe Browsing. VirusTotal as a label is its own subject: see VirusTotal for what a “detected” bit is.

What the papers used instead — 19 of the 27 had researchers look at sites, the others relied on a classifier or a list:

• Hand review of a sample — 19 of 27 papers here had researchers look at the sites. Double and Nothing's keyword filter produced about 4% false positives, which analysts removed with a screenshot dashboard [5Li, Xigao; Yepuri, Anurag; Nikiforakis, Nick (2023): "Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]; Beyond Phish had three experts relabel 2,000 sites (1.98% false positives, 1.63% false negatives) [6Bitaab, Marzieh; Cho, Haehyun; Oest, Adam; Lyu, Zhuoer; Wang, Wei; Abraham, Jorij; Wang, Ruoyu; Bao, Tiffany; Shoshitaishvili, Yan; Doupé, Adam (2023): "Beyond Phish: Toward Detecting Fraudulent e-Commerce Websites at Scale", in: Proceedings of the IEEE Symposium on Security and Privacy. (DOI)].
• A trained classifier validated on a labelled sample — 9 of 27. LOKI's oracle, a gradient-boosting model over 103 features, exceeds 90% precision and recall in cross-validation [12Paudel, Pujan; Stringhini, Gianluca (2026): "LOKI: Proactively Discovering Online Scam Websites by Mining Toxic Search Queries", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]; the 52,493 sites it then discovered have no ground truth, and the authors corroborate with ScamAdviser and Trustpilot instead.
• An LLM — 1 of 27. Muzammil et al. hand-labelled 300 sites and compared models: GPT-4 at 90% accuracy, Llama 3 70B at 87%, a hybrid routing inconclusive cases to GPT-4 at 88%, which is what they ran [9Muzammil, Muhammad; Pitumpe, Abisheka; Li, Xigao; Rahmati, Amir; Nikiforakis, Nick (2025): "The Poorest Man in Babylon: A Longitudinal Study of Cryptocurrency Investment Scams", in: Proceedings of the ACM Web Conference. (DOI)].
• A commercial trust score as the label — Kotzias et al. took ScamAdviser domains with a trust score up to 10, removed Tranco top-1M domains, and kept those with at least two VirusTotal detections [10Kotzias, Platon; Pachilakis, Michalis; Iuit, Javier Aldana; Caballero, Juan; Sanchez-Rola, Iskander; Bilge, Leyla (2025): "Ctrl+Alt+Deceive: Quantifying User Exposure to Online Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]. Their own check of the service's industry tags found high precision for only 7 of 26 categories — the authors add that “this low accuracy is not specific to ScamAdviser, but plagues most commercial website classification services”. Label sources for topics are Website classification.

The scam type is a label too, and nobody shares a taxonomy. Kotzias et al. examine “seven popular scam types” and derived them partly from ScamAdviser's industry tags [10Kotzias, Platon; Pachilakis, Michalis; Iuit, Javier Aldana; Caballero, Juan; Sanchez-Rola, Iskander; Bilge, Leyla (2025): "Ctrl+Alt+Deceive: Quantifying User Exposure to Online Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]; LOKI maps its seeds onto Trustpilot categories and reports ten broad scam categories [12Paudel, Pujan; Stringhini, Gianluca (2026): "LOKI: Proactively Discovering Online Scam Websites by Mining Toxic Search Queries", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]; the population table below uses a seven-value hand taxonomy of its own. None has been validated against another. Exposure and harm figures are only comparable across papers within a type, so report the taxonomy you used and who assigned it.

Scam infrastructure filters visitors, and the filter is aimed at the vantage a research crawler most often has: a cloud IP, a headless browser, a desktop user agent, no referrer.

• IP. Miramirkhani et al.'s campus crawler found 95.7% of the tech-support scam domains that all three of their crawlers found; the two cloud crawlers were filtered by the ad networks [4Miramirkhani, Najmeh; Starov, Oleksii; Nikiforakis, Nick (2017): "Dial One for Scam: A Large-Scale Analysis of Technical Support Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]. Two of Vadrevu and Perdisci's eleven ad networks appeared never to serve social-engineering ads to their university, Tor or AWS addresses, so they put crawlers on laptops on residential networks [19Vadrevu, Phani; Perdisci, Roberto (2019): "What You See is NOT What You Get: Discovering and Tracking Social Engineering Attack Campaigns", in: Proceedings of the ACM Internet Measurement Conference. (DOI)]. Szurdi et al. found more than twice as many malicious pages with a pool of IPs as with one, and when they were shown a benign or error page instead, “we face cloaking 86% of the time and are explicitly blocked only 14% of the time” [7Szurdi, Janos; Luo, Meng; Kondracki, Brian; Nikiforakis, Nick; Christin, Nicolas (2021): "Where are you taking me?Understanding Abusive Traffic Distribution Systems", in: Proceedings of the ACM Web Conference. (DOI)].
• Device. Malicious mobile push notifications “were much more likely to appear on real Android devices, rather than emulated environments” [20Subramani, Karthika; Yuan, Xingzi; Setayeshfar, Omid; Vadrevu, Phani; Lee, Kyu Hyung; Perdisci, Roberto (2020): "When Push Comes to Ads: Measuring the Rise of (Malicious) Push Advertising", in: Proceedings of the ACM Internet Measurement Conference. (DOI)]; Szurdi et al. found tech-support scams shown more to desktop users and survey scams more to phones, and “no evidence” of emulator detection in their traffic distribution systems. Your device choice picks your scam mix.
• User agent and referrer. Liu et al. met four kinds of cloaking on giveaway sites in a pilot: 403s for an institutional network, 403s for browsers not on Windows or Mac, front pages that need a click, and Cloudflare bot checks [8Liu, Enze; Kappos, George; Mugnier, Eric; Invernizzi, Luca; Savage, Stefan; Tao, David; Thomas, Kurt; Voelker, Geoffrey M.; Meiklejohn, Sarah (2024): "Give and Take: An End-To-End Investigation of Giveaway Scam Conversion Rates", in: Proceedings of the ACM Internet Measurement Conference. (DOI)]. Srinivasan et al. kept the search engine as the referrer instead of clicking ads, and warn that their PhantomJS crawler “can, in principle, be detected by scammers” [11Srinivasan, Bharat; Kountouras, Athanasios; Miramirkhani, Najmeh; Alam, Monjur; Nikiforakis, Nick; Antonakakis, Manos; Ahamad, Mustaque (2018): "Exposing Search and Advertisement Abuse Tactics and Infrastructure of Technical Support Scammers", in: Proceedings of the ACM Web Conference. (DOI)].
• Crawler versus search engine. Scam pages show search-engine crawlers something else: NOKEScam pages showed Baidu's crawler timely news to get indexed and showed victims the fraud [25Liu, Mingxuan; Zhang, Yunyi; Wu, Lijie; Liu, Baojun; Hong, Geng; Zhang, Yiming; Jiang, Hui; Zhang, Jia; Duan, Haixin; Zhang, Min; Guan, Wei; Shi, Fan; Yang, Min (2025): "NOKEScam: Understanding and Rectifying Non-Sense Keywords Spear Scam in Search Engines", in: Proceedings of the USENIX Security Symposium. (Link)]; Wang et al. found a store loaded as an iframe over the doorway page, which defeats redirect-based cloaking detection [3Wang, David Y.; Der, Matthew F.; Karami, Mohammad; Saul, Lawrence K.; McCoy, Damon; Savage, Stefan; Voelker, Geoffrey M. (2014): "Search + Seizure: The Effectiveness of Interventions on SEO Campaigns", in: Proceedings of the ACM Internet Measurement Conference. (DOI)]. Measuring this is the Cloak of Visibility design [29Invernizzi, Luca; Thomas, Kurt; Kapravelos, Alexandros; Comanescu, Oxana; Picod, Jean-Michel; Bursztein, Elie (2016): "Cloak of Visibility: Detecting When Machines Browse a Different Web", in: Proceedings of the IEEE Symposium on Security and Privacy. (DOI)] and the older Dagger crawler [30Wang, David Y.; Savage, Stefan; Voelker, Geoffrey M. (2011): "Cloak and dagger: dynamics of web search cloaking", in: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. (DOI)].

Of the 27 papers, 7 measured cloaking by comparing vantages or identities, 8 set the crawler up to look like a victim without measuring the difference, and 12 say nothing. Two browser defences since 2025 make this worse from the other side: Chrome's Enhanced Protection uses the on-device Gemini Nano model against tech-support scam pages (announced 2025-05-08), and in September 2025 Google said it would extend this to sites using “fake viruses or fake giveaways” — whether that has shipped was not established here,⁶⁾ and Edge's scareware blocker was on by default on most Windows and Mac devices by 2025-10-31.⁷⁾ A scam page your crawler reaches may already be intercepted for a real user of those browsers; say which browser, and which protection setting, your exposure estimate assumes.

What to do, and where the detail lives: the detection surface and how to measure your block rate is Crawler detection; residential and mobile vantage points, and the ethics of renting one, are Residential and mobile proxies; cloaking against anti-phishing crawlers is on Cloaking. The one scam-specific rule: record, per URL, which identity saw what, so that a benign page is a reading from one vantage, not a verdict.

The papers that measured harm used seven instruments. None gives a loss total for the victims; the two that see individual payments — leaked back ends and victim reports — see them for one program or one complainant at a time, and each instrument has a known bias:

Both tables name examples; the full per-paper coding is in What the 27 papers did.

Two extrapolations deserve a warning because they are the numbers that get quoted. Li et al. (NDSS 2024) estimate that the scammer accounts behind YouTube comment scams “have potentially stolen more than 100 million US dollars”, from 31 of 72 contacted scammers' wallets [23Li, Xigao; Rahmati, Amir; Nikiforakis, Nick (2024): "Like, Comment, Get Scammed: Characterizing Comment Scams on Media Platforms", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]; Muzammil et al. reach “more than 100M USD” by multiplying the average inflow per site with a known wallet across all detected sites [9Muzammil, Muhammad; Pitumpe, Abisheka; Li, Xigao; Rahmati, Amir; Nikiforakis, Nick (2025): "The Poorest Man in Babylon: A Longitudinal Study of Cryptocurrency Investment Scams", in: Proceedings of the ACM Web Conference. (DOI)]. Both are labelled as estimates in the papers. Quote the measured inflow with its denominator, and the extrapolation only with its assumption.

For wallet-based estimates in general, [28Gómez, Gibran; Liebergen, Kevin van; Caballero, Juan (2023): "Cybercrime Bitcoin Revenue Estimations: Quantifying the Impact of Methodology and Coverage", in: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. (DOI)] re-implemented the published revenue methodologies over 30,424 payment addresses and found that the revenue “is not always underestimated. There exist methodologies that can introduce huge overestimation.” Read it before choosing between co-occurrence windows, address clustering and all-inflows.

Official loss statistics are reports, not measurements, and a paper introduction should say which kind it cites. The FBI's IC3 recorded $20.877 billion in reported losses for 2025, with investment fraud the largest category and tech and customer support among the next;⁸⁾ these are complaints filed. The Global Anti-Scam Alliance's “$442 Billion” is a survey of 46,000 people in 42 markets, scaled up, and the report labels it “estimated”.⁹⁾ The UK's national reporting service changed from Action Fraud to Report Fraud on 4 December 2025, so a UK series crossing that date crosses a system change.¹⁰⁾

The payment and conversion questions push you towards the scammer. Here is what the papers did and what their ethics statements say — which is also what a programme committee will compare you against. The general checklist is Ethics; telling a host, registrar or processor is Notifying websites.

Two decisions the papers disagree on, and you will have to make:

• Report now, or observe? Li et al. “chose not to tamper with the ecosystem while studying it, to avoid measuring artifacts of our own intervention” [5Li, Xigao; Yepuri, Anurag; Nikiforakis, Nick (2023): "Double and Nothing: Understanding and Detecting Cryptocurrency Giveaway Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]; Muzammil et al. withheld reports for the same reason and released data afterwards [9Muzammil, Muhammad; Pitumpe, Abisheka; Li, Xigao; Rahmati, Amir; Nikiforakis, Nick (2025): "The Poorest Man in Babylon: A Longitudinal Study of Cryptocurrency Investment Scams", in: Proceedings of the ACM Web Conference. (DOI)]; Liu et al. analysed retrospectively and note they therefore could not warn victims [8Liu, Enze; Kappos, George; Mugnier, Eric; Invernizzi, Luca; Savage, Stefan; Tao, David; Thomas, Kurt; Voelker, Geoffrey M.; Meiklejohn, Sarah (2024): "Give and Take: An End-To-End Investigation of Giveaway Scam Conversion Rates", in: Proceedings of the ACM Internet Measurement Conference. (DOI)]; ScamMagnifier and NOKEScam reported as they went [2Bitaab, Marzieh; Karimi, Alireza; Lyu, Zhuoer; Oest, Adam; Kuchhal, Dhruv; Saad, Muhammad; Ahn, Gail-Joon; Wang, Ruoyu; Bao, Tiffany; Shoshitaishvili, Yan; Doupé, Adam (2025): "ScamMagnifier: Piercing the Veil of Fraudulent Shopping Website Campaigns", in: Proceedings of the Network and Distributed System Security Symposium. (Link)] [25Liu, Mingxuan; Zhang, Yunyi; Wu, Lijie; Liu, Baojun; Hong, Geng; Zhang, Yiming; Jiang, Hui; Zhang, Jia; Duan, Haixin; Zhang, Min; Guan, Wei; Shi, Fan; Yang, Min (2025): "NOKEScam: Understanding and Rectifying Non-Sense Keywords Spear Scam in Search Engines", in: Proceedings of the USENIX Security Symposium. (Link)]. There is no consensus; state your choice and why.
• Residential IPs. Szurdi et al. did not use residential addresses “to avoid ethical quandaries” [7Szurdi, Janos; Luo, Meng; Kondracki, Brian; Nikiforakis, Nick; Christin, Nicolas (2021): "Where are you taking me?Understanding Abusive Traffic Distribution Systems", in: Proceedings of the ACM Web Conference. (DOI)]; Vadrevu and Perdisci and Liu et al. used them because the ad networks and scam sites filter everything else. The trade-off is on Residential and mobile proxies.

For automated chat with fraud crews as a method — and its limits — see [32Wang, Peng; Liao, Xiaojing; Qin, Yue; Wang, XiaoFeng (2020): "Into the Deep Web: Understanding E-commerce Fraud from Autonomous Chat with Cybercriminals", in: Proceedings of the Network and Distributed System Security Symposium. (Link)] and the Craigslist scam-baiting study [33Park, Youngsam; Jones, Jackie; McCoy, Damon; Shi, Elaine; Jakobsson, Markus (2014): "Scambaiter: Understanding Targeted Nigerian Scams on Craigslist", in: Proceedings of the Network and Distributed System Security Symposium. (Link)]. Neither measures a scam website, so neither is in the population below.

In the extraction's own ethics field, 4 of the 27 papers report an approved review, 14 mention none, 2 discuss ethics and state no review was sought, 2 state none was required, 1 sought review with no stated outcome, and 4 have no ethics record at all.

The corpus runs to 2026, but 2025–2026 are its thinnest and provisional years (CCS and IMC 2026 have not been held; IEEE S&P and TheWebConf 2026 are incompletely indexed). The population has 4 papers from 2025 and 1 from 2026. The status column says whether a method is used by a 2023–2026 paper in these seven venues; where a row adds a judgement about whether you should use it, the judgement is marked as such.

The LLM row is where to be careful. Corpus-wide, papers with an LLM classification step went from 2 of 719 in 2023 (0.3%) to 27 of 690 in 2024 (3.9%), 77 of 770 in 2025 (10.0%) and 71 of 415 in 2026 (17.1%); among this population's five 2025–2026 papers, 1 classifies scam sites with an LLM. The scam literature is not behind the corpus rate — it is too small to have a rate. That one paper reports that the open model it ran was three accuracy points behind GPT-4 at no API cost; it does not show that an LLM beats a trained classifier on scam sites, and nothing in this population tests that. Industry moved faster: Chrome runs an on-device LLM against tech-support scam pages (above). If you use an LLM as the classifier, do what Muzammil et al. did — hand-label a balanced sample first and report accuracy against it. Outside the seven venues the agent form is already published: ScamFerret, an LLM agent that fetches a site's content, DNS records and user reviews, reports 0.972 accuracy on four English scam types with GPT-4 (DIMVA 2025).¹¹⁾

Everything below is a claim about seven venues — CCS, IMC, NDSS, PETS, USENIX Security, TheWebConf and IEEE S&P, 2010–2026, 5,859 extracted papers. The APWG Symposium on Electronic Crime Research (eCrime), EuroS&P, ACSAC, RAID, AsiaCCS, DIMVA, CHI and SOUPS are absent. eCrime and DIMVA publish scam measurement — both outside papers cited on this page appeared there in 2025 — so a scam paper missing from these counts may simply be outside the corpus. See Corpus. 2025–2026 are provisional.

Five probes — the 2026-09-22 gap-pass regex, the bare word scam ten or more times, named scam types, the 2011–2016 spam-value-chain vocabulary, and the extraction's own free-text fields — produce a 183-paper candidate set. Each candidate was read against a rule written before the verdicts were counted:

• IN — the measured objects include web-delivered scams (websites, landing pages, or the ad, search or redirect chains that deliver them) whose purpose is to make the visitor pay or transfer money under false pretences, and the paper collects instances and measures, groups, labels or detects them.
• CONTEXT — scams, but not a scam website as the object: platform accounts and comments, on-chain scam tokens, phone and SMS, scam apps, susceptibility studies, credential or signature theft called phishing, search abuse or scam ads whose destinations are not mostly scams, back-office studies.
• Two clauses added after review. The 2011–2014 spam- and SEO-advertised pharmacy and counterfeit storefront papers are IN as a named lineage for the methods they introduced; later search poisoning that promotes openly illicit trade (controlled drugs, gambling) is CONTEXT. The line there is history, not the object, and a reader could reasonably move the 2022 illicit-drug search-poisoning paper IN. Scareware counts when the paper measures the purchase page, not the software download.
• OUT — scams incidental, fraud against a platform or merchant, malware delivery, or a homonym (Scamper, the topology tool, accounts for several).

Of the 27, 26 include the web platform and 24 ran a crawl.

How well each probe finds the population:

The gap pass chose this page on 43 papers (27 web, 21 from 2024–2026), “the steepest recent slope in the pass”. The regex re-runs to exactly those numbers, but the slope is not scam-website measurement: of the 23 gap-probe hits from 2024–2026 in the collapsed full text, 7 are in the population; of the other 16, thirteen are platform, on-chain, user-study, telephony and LLM-conversation papers, one is on the phishing side of the boundary, and two mention scams only in passing. And the probe misses 9 of the 27 — all from 2010–2014, papers that say pharmacy, counterfeit, affiliate program or one-click fraud rather than the regex's phrases.

*provisional. By venue: IMC 6, NDSS 6, USENIX Security 5, TheWebConf 4, CCS 3, IEEE S&P 3, PETS 0. The literature holds at three to seven papers per bucket and is not growing; the 2025–2026 bucket is already as large as most complete ones, which becomes a trend only once those venue-years are complete.

Hand-coded from each paper's full text; the codes and their quotes are on the provenance page. A paper can use several values.

Each figure is the paper's own, with its own denominator, checked against the paper's text. Lifetimes are not comparable across rows: Kotzias et al.'s is “the activity time from first to last observations” in telemetry — it ends when users stop arriving; Srinivasan et al.'s is “the difference between the earliest and most recent date that the domain was seen hosting TSS content” — bounded by the crawl's cadence; Leontiadis et al.'s is a survival curve over presence in search results, truncated at the 192 days they observed.

Every corpus number is a count of papers, from the 5,859-paper extraction, with its denominator named. The 27 is a hand verdict over a 183-paper candidate set; the report script exits if the candidates and the verdicts diverge in either direction. The method table is a hand coding of those 27 papers from their full text by the author of this page, drafted from structured reading notes and not double-coded; treat single-paper differences as soft. Full-text probes read paper.cols.txt (4 of 5,859 papers have none). Per-paper figures were checked against the papers' text, not the extraction's summaries. The probes, the verdict list with a reason per paper, the codes, the unedited report output and the external checks are on online_scams; corpus-wide caveats are on Corpus.

• The channel, its window and its bias. “Certificate Transparency, 2026-03-01 to 2026-05-31, domains matching a 40-keyword list” — and the sentence that says what that channel cannot see.
• Sites, and operators. Report both counts and the grouping key. If you did not group, say so, and do not call a domain count a campaign count.
• The label and its validation. Who looked at how many sites, and the false-positive rate on that sample. If a blocklist or trust score is the label, do not also report that blocklist's “coverage”.
• Blocklist coverage with its date. Which list, when you queried it, and how long after your detection.
• Crawler identity per visit. IP type, device, user agent, referrer, interaction, and whether you compared vantages. A cloaking verdict per URL, not a limitation paragraph.
• Lifetime with its definition. What “alive” meant (resolves, answers, still serves the scam, still visited), how often you re-visited, and the share still alive when you stopped — a right-censored number, not a median of what you happened to see die.
• The type taxonomy. Which scam types, who assigned them, and whether they are comparable to another paper's.
• Harm as a bracket. Wallet inflows with the attribution window and the all-inflows upper bound; checkout visits as intent; any extrapolation with its assumed rate stated in the same sentence.
• Interaction and reporting. Whether you paid, called, chatted, created accounts or clicked ads; the review outcome; and whether you reported sites during the study.

• Scam types that arrived after the corpus's coverage thins. Toll-payment scams are absent here; an eCrime 2025 paper counts 67,907 toll-scam domains, mostly registered in 2025, with 86.9% in five uncommon TLDs.¹²⁾
• No paper in this population since 2018 is about tech-support scams as such; after that they appear only as one category among social-engineering ads, the latest in 2023 [21Yang, Zheng; Allen, Joey; Landen, Matthew; Perdisci, Roberto; Lee, Wenke (2023): "TRIDENT: Towards Detecting and Mitigating Web-based Social Engineering Attacks", in: Proceedings of the USENIX Security Symposium. (Link)]. Browser vendors built dedicated defences in 2025. Whether the scams moved, shrank, or stayed and went unmeasured, this corpus cannot say.
• No PETS paper is in the population. That is a finding about seven venues and 27 papers, not about PETS.
• No paper in this population compares an LLM classifier with a trained classifier on the same scam set.
• Mobile. The scam feeds are desktop-heavy [10Kotzias, Platon; Pachilakis, Michalis; Iuit, Javier Aldana; Caballero, Juan; Sanchez-Rola, Iskander; Bilge, Leyla (2025): "Ctrl+Alt+Deceive: Quantifying User Exposure to Online Scams", in: Proceedings of the Network and Distributed System Security Symposium. (Link)], and the design that used a real phone saw malicious push notifications there that emulators did not [20Subramani, Karthika; Yuan, Xingzi; Setayeshfar, Omid; Vadrevu, Phani; Lee, Kyu Hyung; Perdisci, Roberto (2020): "When Push Comes to Ads: Measuring the Rise of (Malicious) Push Advertising", in: Proceedings of the ACM Internet Measurement Conference. (DOI)].
• Non-English. One-click fraud (Japanese) and NOKEScam (Chinese, inside Baidu) are the only non-English scam populations here, and the NOKEScam release domain no longer resolves.

• Security — namespace outline.
• Phishing — credential theft, feeds and phishing cloaking; the boundary is what the victim hands over.
• VirusTotal — what a “detected” bit is.
• Platforms — scam accounts and comments as the object.
• Crawler detection — the detection surface, and measuring your block rate.
• Crawling location — which vantage point, and residential proxies.
• Advertising — scam ads measured as ad exposure.
• Ownership resolution — why WHOIS is not an owner.
• Ethics and Notifying websites — review and reporting.

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