175.106.10.195 Threat Intelligence - Indonesia | IP Address Lookup

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General

IP Address
175.106.10.195
IPv4 Address
Location
🇮🇩 North Jakarta, Indonesia
ID
Network
AS46023
PT Quantum Tera Network
Threat Score
40/100
Medium Risk
Aggressive-DetectionbruteforceBruteforceBrute-ForceConnection-Resetdigital oceanmalwarepayload-delivery
Attack Intelligence
MITRE ATT&CK Techniques
T1059 - Command and Scripting Interpreter, T1105 - Ingress Tool Transfer
Noticed
11 times
Protocols Attacked
combined portscan ssh
Open Ports Detected
1101232538946558780993995
Geographic Location
Country
Indonesia
City
North Jakarta
Region
Jakarta
Coordinates
-6.1272, 106.9198
Network Information
ASN
AS46023
Organization
PT Quantum Tera Network
Network
AS46023 PT Quantum Tera Network
WHOIS Information
inetnum
175.106.8.0 - 175.106.15.255
netname
QUANTUMNET-ID
descr
PT Quantum Tera Network
country
ID
admin-c
TW558-AP
tech-c
TW558-AP
status
ALLOCATED PORTABLE
mnt-by
MNT-APJII-ID
mnt-lower
MAINT-ID-QUANTUMNET
mnt-routes
MAINT-ID-QUANTUMNET
mnt-irt
IRT-QUANTUMNET-ID
last-modified
2013-04-08T04:00:44Z
irt
IRT-QUANTUMNET-ID
address
PT Quantum Tera Network
e-mail
hostmaster@quantum.net.id
abuse-mailbox
hostmaster@quantum.net.id
person
Tjandra Widjaja
nic-hdl
TW558-AP
phone
+62-21-53678696
fax-no
+62-21-53678697
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 175.106.10.195 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.