103.54.94.222 Threat Intelligence - Indonesia | IP Address Lookup

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General

IP Address
103.54.94.222
IPv4 Address
Location
🇮🇩 Indonesia
ID
Network
AS133823
PT Infokom Elektrindo
Threat Score
35/100
Medium Risk
Aggressive-DetectionbruteforceBruteforceBrute-ForceConnection-Resetdigital oceanportscanProtocol-Probing
Attack Intelligence
Noticed
12 times
Protocols Attacked
combined portscan ssh
Open Ports Detected
161389465587707184439100995
Geographic Location
Country
Indonesia
City
Unknown
Region
Unknown
Coordinates
-6.1750, 106.8286
Network Information
ASN
AS133823
Organization
PT Infokom Elektrindo
Network
AS133823 PT Infokom Elektrindo
WHOIS Information
inetnum
103.54.92.0 - 103.54.95.255
netname
INFOKOM
descr
PT Infokom Elektrindo
admin-c
RR36-AP
tech-c
RR36-AP
country
ID
mnt-by
MNT-APJII-ID
mnt-lower
MAINT-ID-INFOKOM
mnt-irt
IRT-INFOKOM-ID
mnt-routes
MAINT-ID-INFOKOM
status
ALLOCATED PORTABLE
last-modified
2016-10-05T09:24:05Z
irt
IRT-INFOKOM-ID
address
PT Infokom Elektrindo
e-mail
inadmin@infokom.id
abuse-mailbox
abuse@infokom.id
person
Rafdian rasyid
phone
+62-21-526-0610
fax-no
+62-21-526-0620
nic-hdl
RR36-AP
route
103.54.94.0/24
origin
AS133823
Attack Logs
DateTarget LocationProtocolLink
2026-09-04 Digitaloceantoronto Combined Multiple View Log
2026-09-04 Toronto, Canada SSH View Log
2026-09-03 Digitaloceantoronto Combined Multiple View Log
2026-09-03 Toronto, Canada SSH View Log
2026-09-02 Digitaloceantoronto Combined Multiple View Log
2026-09-02 Toronto, Canada SSH View Log
2026-09-01 Toronto, Canada SSH View Log
2026-09-01 Digitaloceantoronto Combined Multiple View Log
Disclaimer
This page contains threat intelligence information for the IPv4 address 103.54.94.222 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.