103.60.180.150 Threat Intelligence - Indonesia | IP Address Lookup

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General

IP Address
103.60.180.150
IPv4 Address
Location
🇮🇩 Indonesia
ID
Network
AS55685
PT Jala Lintas Media
Threat Score
35/100
Medium Risk
Aggressive-DetectionbruteforceBruteforceBrute-ForceConnection-Resetdigital oceanportscanProtocol-Probing
Attack Intelligence
Noticed
12 times
Protocols Attacked
combined portscan ssh
Passive DNS
mersi.co.id
Open Ports Detected
110111143253894434655877071993995
Geographic Location
Country
Indonesia
City
Unknown
Region
Unknown
Coordinates
-6.1750, 106.8286
Network Information
ASN
AS55685
Organization
PT Jala Lintas Media
Network
AS55685 PT Jala Lintas Media
WHOIS Information
inetnum
103.60.180.0 - 103.60.183.255
netname
JLMNET-ID
descr
PT Jala Lintas Media
admin-c
AP549-AP
tech-c
AP549-AP
country
ID
mnt-by
MNT-APJII-ID
mnt-lower
MAINT-ID-JLM
mnt-irt
IRT-JLM-ID
mnt-routes
MAINT-ID-JLM
status
ALLOCATED PORTABLE
last-modified
2015-06-15T08:56:29Z
irt
IRT-JLM-ID
address
PT Jala Lintas Media
e-mail
abuse@jlm.net.id
abuse-mailbox
abuse@jlm.net.id
person
Aditya Pradana
phone
+62-21-87906175
nic-hdl
AP549-AP
fax-no
+62-21-87906192
route
103.60.180.0/24
origin
AS55685
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 103.60.180.150 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.