103.50.24.17 Threat Intelligence - Indonesia | IP Address Lookup

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General

IP Address
103.50.24.17
IPv4 Address
Location
🇮🇩 Bandar Lampung, Indonesia
ID
Network
AS63869
PT MERAH PUTIH TELEMATIKA
Threat Score
35/100
Medium Risk
Aggressive-DetectionbruteforceBruteforceBrute-ForceConnection-Resetdigital oceanportscanProtocol-Probing
Attack Intelligence
Noticed
9 times
Protocols Attacked
combined portscan ssh
Open Ports Detected
110112111432538944346552695877071844390909100993995
Geographic Location
Country
Indonesia
City
Bandar Lampung
Region
Lampung
Coordinates
-5.4291, 105.2615
Network Information
ASN
AS63869
Organization
PT MERAH PUTIH TELEMATIKA
Network
AS63869 PT MERAH PUTIH TELEMATIKA
WHOIS Information
inetnum
103.50.24.0 - 103.50.27.255
netname
MERAHPUTIH-ID
descr
PT MERAH PUTIH TELEMATIKA
admin-c
HW2256-AP
tech-c
HW2256-AP
country
ID
mnt-by
MNT-APJII-ID
mnt-lower
MAINT-ID-MERAHPUTIH
mnt-irt
IRT-MERAHPUTIH-ID
mnt-routes
MAINT-ID-MERAHPUTIH
status
ALLOCATED PORTABLE
geoloc
05.3899049 105.2155748
last-modified
2024-08-08T03:43:11Z
irt
IRT-MERAHPUTIH-ID
address
PT MERAH PUTIH TELEMATIKA
e-mail
cs@merahputih.net.id
abuse-mailbox
cs@merahputih.net.id
person
Her Wanto
phone
+6285769810079
nic-hdl
HW2256-AP
route
103.50.24.0/24
origin
AS63869
Attack Logs
DateTarget LocationProtocolLink
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 103.50.24.17 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.