Security1 distinct publisher2 min readPublished
Job lures arrive by email, romance by social media, tech support by phone, across three months of one vendor's telemetry. The counts measure delivery; conversion, where the training math gets harder, isn't part of what's tracked.
The Watch · Security desk

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The pairing is the part with operational consequences. Job lures land where work mail lands. Romance fraud shows up where meeting strangers is normal, and tech support fraud arrives down a phone line [2]. Those sit behind different controls, and only one of them is a mail gateway.
The hourly numbers are steeper than the percentage reads. Malwarebytes puts the scam text peak at 12:00 pm ET, about 874 percent above the 1:00 am ET trough [5]. Add the baseline back and divide: (100 + 874) / 100, so noon carries roughly 9.7 times the volume of the quietest hour [11]. That points to deliberate scheduling behind the bursts.
Provenance matters here more than the individual percentages. Everything comes from one vendor's view of its own install base over a 91-day window [12]. The brand ranking, with Google abused at least twice as often as Amazon, comes from user reports rather than product telemetry [8]. The 500,000 phishing sites blocked per day is the company's own figure [4]; taken at face value across the window, that is about 45.5 million sites [13]. No second data set in the material corroborates any of it.
Outcomes are missing from this data set entirely. Every reported metric is a delivery count or a share of impersonations [15]. So the specialization Malwarebytes describes [10] is a statement about where scams arrive, not about which arrivals convert or what they cost. A program funded against losses cannot rank its channels from this.
It can still see that the targets are unevenly distributed. Generic awareness content spends the same minute on the same generic warning for a recruiter working an inbox and a help desk taking inbound calls, while the telemetry says the lure reaching each of them travels a different route [2]. The web is still the widest doorway, ahead of email and SMS [3], and that is the one channel where a blocklist does most of the work without a human in the loop.
The fastest-moving segment reported sits furthest from anything a security team owns. Roblox impersonation activity rose 15 percent and Steam 19 percent between mid-June and mid-July, with Discord and Minecraft also among the most impersonated gaming platforms [9]. Those are household accounts. So is most of the surface where MrBeast's likeness, present in about 30 percent of impersonation scams in this data [7], does its work.
Ranked by verification strength, evidence, and original report placement.
Malwarebytes analyzed its own threat data collected between April 15 and July 14, 2026, and tracked more than 20 scam categories, from tech support cons to sextortion.
Malwarebytes researchers said the platforms favored often match the content of a scam: job scams mostly arrive through typical work channels like email, romance scams mostly arrive through social media, and tech support scams mostly arrive through the phone.
The web remains the top delivery method for scams, ahead of email and SMS.
Scam texts peak at 12:00 pm ET, a volume about 874% higher than the quietest hour, 1:00 am ET.
Scam text volume is lowest on Sunday and rises steadily until it peaks on Friday, when people get about 50% more fraudulent texts than at the start of the week; Malwarebytes says this suggests scammers time their campaigns rather than send at random.
Jimmy Donaldson, known as MrBeast, is the most impersonated person in Malwarebytes' data, ahead of Elon Musk and Donald Trump, with his likeness appearing in about 30% of impersonation scams, ranging from crypto giveaways to transfer-fee schemes.
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Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
One vendor's logs, relayed intact
The 874% noon spike, MrBeast's 30% share, the 15% and 19% gaming jumps — every one of these comes from Malwarebytes reading its own telemetry, and Help Net Security prints them without an independent check. There are no absolute counts, no denominators, and no account of how a message gets filed under one of 20-plus scam categories, so the percentages can be compared to each other and to nothing else. The brand ranking rests on what Malwarebytes users chose to report to Malwarebytes.
Nothing here tracks uptake
This is a measurement of criminal behaviour, not of anything being deployed or taken up: no carrier, platform or security team in this reporting has changed a control in response to the findings. The only operational figure on offer describes Malwarebytes' own blocking volume, and treating a vendor's product statistic as traction would be dressing up marketing, so we leave this unscored.
Framing outruns the arithmetic
Two stretches stand out. '874% higher than the 1:00 am trough' is the same fact as 'about 9.7 times', picked because it reads bigger. And the headline claim that scammers have figured out the best time to text you converts a diurnal volume curve — which tracks when people are awake and holding a phone — into proven strategy. Malwarebytes' closing verdict that the scam economy is becoming 'more specialized' is offered without any earlier period to compare against, and the delivery-only metrics quietly stand in for effectiveness.
The remedy vendor drew the map
A consumer anti-malware company published a study about how much scam traffic is out there, and it lands where you would expect: the collection net is its own install base, the brand ranking comes from its own users' reports, and the single hard number in the piece is the half-million sites its product claims to stop each day. None of that makes the channel pattern wrong. It does mean the categories, the sample and the headline all belong to the party selling the fix, and Help Net Security flags none of it.
Confident about the quotes, not the numbers
One outlet, one data owner, no way in. Help Net Security reproduces the report faithfully enough that we are sure what Malwarebytes said and when it said it, and the internal arithmetic holds. Judging whether these shares describe scam traffic generally, rather than traffic reaching people who install consumer anti-malware and file reports, is beyond what a single pass-through account can support.