103.101.224.37 Threat Intelligence - Indonesia | IP Address Lookup

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General

IP Address
103.101.224.37
IPv4 Address
Location
🇮🇩 Indonesia
ID
Network
AS134612
PT Atria Teknologi Indonesia
Threat Score
45/100
Medium Risk
Aggressive-DetectionbruteforceBruteforceBrute-ForceConnection-Resetdigital oceanportscanProtocol-Probing
Attack Intelligence
Noticed
10 times
Protocols Attacked
combined portscan ssh
Open Ports Detected
11014322253894434655222526953587707180995
Geographic Location
Country
Indonesia
City
Unknown
Region
Unknown
Coordinates
-6.1750, 106.8286
Network Information
ASN
AS134612
Organization
PT Atria Teknologi Indonesia
Network
AS134612 PT Atria Teknologi Indonesia
Associated CVEs
WHOIS Information
inetnum
103.101.224.0 - 103.101.227.255
netname
IDNIC-INTERNUSA-ID
descr
PT Internusa Hasta Buana
admin-c
JF1233-AP
tech-c
JF1233-AP
country
ID
mnt-by
MNT-APJII-ID
mnt-routes
MAINT-ID-INTERNUSA
mnt-irt
IRT-INTERNUSA-ID
status
ASSIGNED PORTABLE
last-modified
2025-11-05T04:32:55Z
irt
IRT-INTERNUSA-ID
address
PT Internusa Hasta Buana
e-mail
abuse@internusa.co.id
abuse-mailbox
abuse@internusa.co.id
person
Jason Frisch
phone
+622128565201
nic-hdl
JF1233-AP
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 103.101.224.37 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.