139.170.73.2 Threat Intelligence - China | IP Address Lookup

General

IP Address
139.170.73.2
IPv4 Address
Location
🇨🇳 Xining, China
CN
Network
AS4837
CHINA UNICOM China169 Backbone
Threat Score
38/100
Medium Risk
BlocklistcloudflarecowriedionaeafatthoneytrapmailoneyMalicious-IP
Attack Intelligence
Noticed
4 times
Protocols Attacked
portscan web
Countries Attacked
China, Germany, Russian Federation, United Kingdom of Great Britain and Northern Ireland, United States of America
Geographic Location
Country
China
City
Xining
Region
Qinghai
Coordinates
36.6268, 101.7548
Network Information
ASN
AS4837
Organization
CHINA UNICOM China169 Backbone
Network
AS4837 CHINA UNICOM China169 Backbone
WHOIS Information
NetRange
139.170.0.0 - 139.170.255.255
CIDR
139.170.0.0/16
NetName
APNIC-ERX-139-170-0-0
NetHandle
NET-139-170-0-0-1
Parent
NET139 (NET-139-0-0-0-0)
NetType
Early Registrations, Transferred to APNIC
Organization
Asia Pacific Network Information Centre (APNIC)
RegDate
2010-11-03
Updated
2010-11-17
Comment
This IP address range is not registered in the ARIN database.
Ref
https://rdap.arin.net/registry/ip/139.170.0.0
OrgName
Asia Pacific Network Information Centre
OrgId
APNIC
Address
PO Box 3646
City
South Brisbane
StateProv
QLD
PostalCode
4101
Country
AU
OrgAbuseHandle
AWC12-ARIN
OrgAbuseName
APNIC Whois Contact
OrgAbusePhone
+61 7 3858 3188
OrgAbuseEmail
search-apnic-not-arin@apnic.net
OrgAbuseRef
https://rdap.arin.net/registry/entity/AWC12-ARIN
OrgTechHandle
AWC12-ARIN
Attack Logs
DateTarget LocationProtocolLink
2026-07-16 Cfglobal Web Multiple View Log
Disclaimer
This page contains threat intelligence information for the IPv4 address 139.170.73.2 and was generated either as a result of observed malicious activity or as an information gathering exercise to assist with enrichment of security events and context. All information is gathered passively through aggregation of public sources, or observations through activity upon honeynets. The host score is calculated through a series of statistically weighted values and machine learning which takes into account metadata such as host information, frequency, volume and global distribution of malicious activity, association with other known malicious hosts or networks, proxying or anonymising behaviour such as with tor exit nodes, residential proxies or VPN services, and many other attributes. These values are historical and indicative only, and should not be taken to be an accurate representation of the users, businesses or networks in which they reside.