103.142.240.142 Threat Intelligence - Indonesia | IP Address Lookup

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General

IP Address
103.142.240.142
IPv4 Address
Location
🇮🇩 Jakarta, Indonesia
ID
Network
AS139382
PT Multi Teknologi Telematika
Threat Score
10/100
Low Risk
Attack Intelligence
Protocols Attacked
combined portscan ssh
Passive DNS
mx1.mtt.net.id
Open Ports Detected
1101432546553587707180993995
Geographic Location
Country
Indonesia
City
Jakarta
Region
Jakarta
Coordinates
-6.2121, 106.8319
Network Information
ASN
AS139382
Organization
PT Multi Teknologi Telematika
Network
AS139382 PT Multi Teknologi Telematika
Associated CVEs
WHOIS Information
inetnum
103.142.240.0 - 103.142.241.255
netname
MTT-ID
descr
PT Multi Teknologi Telematika
admin-c
FF355-AP
tech-c
FF355-AP
country
ID
mnt-by
MNT-APJII-ID
mnt-irt
IRT-MTT-ID
mnt-routes
MAINT-ID-MTT
status
ALLOCATED PORTABLE
last-modified
2019-09-10T10:05:51Z
irt
IRT-MTT-ID
address
PT Multi Teknologi Telematika
e-mail
admin@multiteknologi.net.id
abuse-mailbox
abuse@multiteknologi.net.id
person
Ferdinan Ferdinan
phone
+62-812-61706296
nic-hdl
FF355-AP
mnt-lower
MAINT-ID-MTT
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
2026-08-29 Digitaloceantoronto Combined Multiple View Log
2026-08-29 Toronto, Canada SSH View Log
Disclaimer
This page contains threat intelligence information for the IPv4 address 103.142.240.142 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.