Not as a verified, current share of all internet traffic. The 73% figure comes from Arkose Labs, which said its Global Intelligence Network analysis of activity from January through September 2023 found that bad bots and fraud-farm activity accounted for that share. It is a vendor estimate based on its observed activity—not a census establishing what percentage of all internet traffic is malicious automation.
Where the 73% claim comes from
In a November 2023 announcement, Arkose Labs said it analyzed tens of billions of bot attacks collected through its Global Intelligence Network between January and September 2023. It estimated that 73% of internet traffic consisted of bad bots and fraud-farm activity. Arkose Labs’ announcement describes the finding; SecurityWeek’s contemporaneous report characterizes it as an estimate for Q3 2023.
The key limitation is the denominator. The accessible announcement does not explain a global sampling method that would establish the result as a representative count of all internet traffic. The number is best understood as Arkose Labs’ estimate from activity visible to its network, using its categories and methods. It is not a measured 2026 rate, nor proof that nearly three-quarters of every website’s visits are malicious.
Why another report gives a different bot percentage
Imperva’s 2024 Bad Bot Report gives a different picture for a different dataset and scope. A June 2024 summary of that report says Imperva observed 49.6% automated traffic in its global network during 2023, comprising 32% bad bots and 17.6% good bots. Its analysis focuses on application-layer activity (OSI layer 7). The SSL Store’s report summary describes these figures.
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| Source and period | Reported finding | What the figure describes |
|---|---|---|
| Arkose Labs, analysis covering January–September 2023; announced November 2023 | 73% bad bots and fraud-farm activity | Arkose Labs’ estimate from activity observed through its Global Intelligence Network. |
| Imperva, 2023 activity; reported in its 2024 Bad Bot Report | 49.6% automated traffic: 32% bad bots and 17.6% good bots | Traffic observed in Imperva’s global network, focused on application-layer activity, as summarized by The SSL Store. |
These percentages should not be averaged or treated as competing measurements of an identical population. The sources use separate vendor networks, definitions, and observation methods. Imperva’s split also illustrates why “bot traffic” and “bad bot traffic” are not interchangeable: automation can be useful or abusive.
What bad bots do
Arkose Labs identifies fake account creation, account takeover, scraping, account management, and in-product abuse among the common attack categories in its report. SecurityWeek notes that in-product abuse replaced card testing in Arkose’s Q3 list compared with Q2. These are categories in Arkose’s reporting, not a universal ranking of bot threats.
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Imperva’s broader taxonomy includes other examples: price scalping and content scraping can affect sales, original content, performance, and reputation; account takeover and fake-account creation threaten account security; credit-card and gift-card abuse expose businesses to fraud; denial-of-service threatens availability; and inventory or airline-seat squatting can block legitimate buyers. Those examples come from Imperva’s report summary, not Arkose’s five-category list.
What the figures mean for everyday users and businesses
For an individual, the headline does not mean that most page views, messages, or online purchases are necessarily bots. A site’s exposure depends on its audience, services, and defenses. Users may encounter the effects indirectly: fake accounts, stolen-account activity, abusive sign-ups, slowed services, or inventory disappearing through automated purchasing.
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For a business, the useful takeaway is that automated traffic needs to be classified by behavior and risk, not simply counted. Legitimate search crawlers, monitoring tools, and other automation can coexist with scripts attempting credential attacks, scraping, fraud, or service disruption. A high aggregate bot percentage alone does not show how much is harmful to a particular organization.
How organizations can evaluate bot mitigation
Imperva’s report summary discusses web application firewalls (WAFs) and the broader web application and API protection category, alongside monitoring, request-rate limits, login-failure limits, layered authentication, and other controls. These are organizational security measures; no single firewall, home router, or product can be assumed to eliminate sophisticated bot abuse.
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When evaluating a mitigation approach, organizations should compare:
- Coverage: Does it protect the relevant web applications and APIs?
- Classification: Can it distinguish legitimate users and crawlers from abusive automation?
- User impact: How does it manage false positives, added friction, and blocked legitimate activity?
- Operations: What monitoring, response, and support are available?
- Evidence: How are efficacy claims measured, and do those measurements match the organization’s own traffic and threat model?
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Bottom line on the 73% statistic
Arkose Labs estimated that bad bots and fraud-farm activity made up 73% of the traffic visible in its 2023 analysis. Because the stated evidence comes from a vendor network and does not establish a representative census of all internet traffic, the statistic should not be repeated as a current universal fact. Imperva’s separate 2023 estimate—49.6% automated traffic in its network, including both good and bad bots—underscores the importance of attribution and scope rather than validating Arkose’s percentage.
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