156.0.130.122 Threat Intelligence and Host Information

General

IP Address
156.0.130.122
IPv4 Address
Location
🇬🇭 Sunyani, Ghana
GH
Network
AS328224
University-of-Energy-and-Natural-Resourc
Threat Score
33/100
Medium Risk
bruteforcecowriemalicioussftpssh
Attack Intelligence
MITRE ATT&CK Techniques
T1110 - Brute Force
Geographic Location
Country
Ghana
City
Sunyani
Region
Bono
Coordinates
7.3313, -2.3338
Network Information
ASN
AS328224
Organization
University-of-Energy-and-Natural-Resourc
Network
AS328224 University-of-Energy-and-Natural-Resourc
WHOIS Information
NetRange
156.0.0.0 - 156.0.255.255
CIDR
156.0.0.0/16
NetName
AFRINIC-ERX-156-0-0-0
NetHandle
NET-156-0-0-0-1
Parent
NET156 (NET-156-0-0-0-0)
NetType
Transferred to AfriNIC
OriginAS
Organization
African Network Information Center (AFRINIC)
RegDate
2004-05-17
Updated
2015-05-04
Comment
The African & Indian Ocean Internet Registry
Ref
https://rdap.arin.net/registry/entity/AFRINIC
OrgName
African Network Information Center
OrgId
AFRINIC
Address
Lot 19, Cybercity
City
Ebene
StateProv
PostalCode
Country
MU
OrgAbuseHandle
GENER11-ARIN
OrgAbuseName
Generic POC
OrgAbusePhone
+230 4666616
OrgAbuseEmail
abusepoc@afrinic.net
OrgAbuseRef
https://rdap.arin.net/registry/entity/GENER11-ARIN
Attack Logs
Date Target Location Protocol Link
2024-12-17 Perth, Australia MULTIPLE View Log

  • Country: Ghana
  • Network:
  • Noticed: 3 times
  • Protocols Attacked: SSH
  • Countries Attacked: Australia

Malware Detected on Host

Count: 2 9ae6de6e96762506a66732b4e56c6cca31fc562fac3eef8882e07bc2c9c05bab e5cb5df2594dac60dcc2eb0fa7c32f3033bbf59bf43e23e1917e5fc9270ed7b9

Disclaimer
This page contains threat intelligence information for the IPv4 address 156.0.130.122 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.