103.164.174.93 Threat Intelligence - Indonesia | IP Address Lookup
ipinfopage
Share on:
General
IP Address
103.164.174.93
Location
🇮🇩 Indonesia
Network
AS149930
Threat Score
35/100
Attack Intelligence
Noticed
10 times
Protocols Attacked
combined portscan ssh
Open Ports Detected
443
Geographic Location
Country
Indonesia
City
Unknown
Region
Unknown
Coordinates
-6.1750, 106.8286
Network Information
ASN
AS149930
Organization
PT Replay Inti Media
Network
AS149930 PT Replay Inti Media
WHOIS Information
inetnum
103.164.174.0 - 103.164.175.255
netname
IDNIC-MUBAKAB-ID
descr
Dinas Kominfo Musi Banyuasin
admin-c
AAG7-AP
tech-c
AAG7-AP
country
ID
mnt-by
MNT-APJII-ID
mnt-irt
IRT-MUBAKAB-ID
mnt-routes
MAINT-ID-MUBAKAB
status
ASSIGNED PORTABLE
last-modified
2021-03-22T06:13:48Z
irt
IRT-MUBAKAB-ID
address
Dinas Kominfo Musi Banyuasin
e-mail
it@dinkominfo.mubakab.go.id
abuse-mailbox
it@dinkominfo.mubakab.go.id
person
Ahmad Alfar Gani
phone
+62-812-9920-6304
nic-hdl
AAG7-AP
fax-no
+62-812-9920-6304
mnt-lower
MAINT-ID-MUBAKAB
Attack Logs
| Date | Target Location | Protocol | Link |
|---|---|---|---|
| 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 103.164.174.93 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.