163.7.9.55 Threat Intelligence and Host Information

General

IP Address
163.7.9.55
IPv4 Address
Location
🇳🇿 Rotorua, New Zealand
NZ
Network
AS38140
NZ Forest Research Institute Ltd
Threat Score
37/100
Medium Risk
Aggressive-DetectionBrute-ForceBruteforceConnection-ResetCredential-HarvestingENV-HuntingMISPNginx
Attack Intelligence
MITRE ATT&CK Techniques
T1110 - Brute Force
Open Ports Detected
22
Geographic Location
Country
New Zealand
City
Rotorua
Region
Bay of Plenty
Coordinates
-38.1296, 176.2444
Network Information
ASN
AS38140
Organization
NZ Forest Research Institute Ltd
Network
AS38140 NZ Forest Research Institute Ltd
WHOIS Information
inetnum
163.7.0.0 - 163.7.127.254
netname
BYTEPLUS-SG
descr
Byteplus Pte. Ltd.
country
SG
admin-c
BPLA13-AP
tech-c
BPLA13-AP
abuse-c
AB1590-AP
status
ALLOCATED NON-PORTABLE
mnt-by
MAINT-BYTEPLUS-SG
mnt-irt
IRT-BYTEPLUS-SG
last-modified
2025-06-09T09:01:21Z
irt
IRT-BYTEPLUS-SG
address
1 Raffles Quay,
e-mail
bd_abuse@bytedance.com
abuse-mailbox
bd_abuse@bytedance.com
role
Byteplus Pte Ltd administrator
phone
+65-6950-4420
nic-hdl
BPLA13-AP
route
163.7.9.0/24
origin
AS150436

  • Country: New Zealand
  • Network:
  • Noticed: 1 times
  • Protocols Attacked: SSH
  • Countries Attacked: Australia, Malaysia

CVEs Detected

CVE-2006-5051 CVE-2007-2243 CVE-2007-2768 CVE-2008-3844 CVE-2023-51767 CVE-2024-6387 CVE-2025-26465 CVE-2025-26466 CVE-2025-32728 CVE-2026-35385 CVE-2026-35386 CVE-2026-35387 CVE-2026-35388 CVE-2026-35414

Similar IP Addresses Detected

163.7.12.84 163.7.13.180 163.7.13.72 163.7.14.20 163.7.14.89 163.7.15.181 163.7.15.194 163.7.15.251 163.7.16.149 163.7.8.79

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Disclaimer
This page contains threat intelligence information for the IPv4 address 163.7.9.55 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.