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DMARC Spoof Detection, Japan

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This page shows DMARC authentication failures originating from Japan. Learn more about this data.

Japan accounts for 42 DMARC authentication failures observed by sh4meful over the observation window, sourced from 18 distinct IP addresses across 13 networks. This represents approximately 0.3% of total failure volume observed. Activity from Japan has declined over the last 30 days compared with the prior 30-day period. The most active source is KIR with 10 failures across 2 IP addresses.

Failures (Filtered)

42

IPs (Filtered)

18

Networks (Filtered)

13

Messages (Filtered)

50

Top Networks in Japan

Ten most active networks sourcing DMARC failures from Japan:

Network Organization Failures Distinct IPs Top City
KIR โ€” 10 2 โ€”
SAKURA-NET โ€” 12 3 Hiyoshi
RIPE-ERX-147-44-0-0 โ€” 5 3 Tokyo
DIX-CL โ€” 5 1 โ€”
ASAHI-NET โ€” 2 1 Tokorozawa
CSF-NET-CIDR-BLK-JP โ€” 1 1 โ€”
NIAC โ€” 1 1 Nagoya
YAHOO-AP โ€” 1 1 โ€”
OCN โ€” 1 1 โ€”
GOOGLE-CLOUD Google 1 1 Tokyo

KIR and SAKURA-NET together account for 54% of failure volume from this country. Distribution across many networks is consistent with commodity spoofing infrastructure operating from this geography.

Failure Activity Over Time

Peak activity was observed in the week of June 22, 2026 with 3 failures recorded. Activity in the most recent 30-day window declined sharply compared with the prior period (0 vs 6 failures).

Regional Context

Compared with peer geographies in Eastern Asia, Japan's failure volume is below the regional median. Countries in this region collectively contributed 3% of failures observed.

Top Owners in Japan

What This Means

Country-level patterns don't imply that mail from Japan is inherently malicious. Many failures reflect misconfigured legitimate senders, forwarded messages that break authentication, or automated infrastructure operating without authorization. Domain owners investigating a specific failure should look at the source IP and network details rather than the country alone. Our DMARC guide explains how to interpret these signals in your own reports.