104.168.117.131 Threat Intelligence and Host Information

General

IP Address
104.168.117.131
IPv4 Address
Location
🇺🇸 United States
US
Network
AS36352
AS-COLOCROSSING
Threat Score
46/100
Medium Risk
bruteforcebrute-forcebruteforcecowriesshtcp
Attack Intelligence
MITRE ATT&CK Techniques
T1078 - Valid Accounts, T1083 - File and Directory Discovery, T1098.004 - SSH Authorized Keys, T1105 - Ingress Tool Transfer, T1110.004 - Credential Stuffing, T1110 - Brute Force
Open Ports Detected
22
Geographic Location
Country
United States
City
Unknown
Region
Unknown
Coordinates
37.7510, -97.8220
Network Information
ASN
AS36352
Organization
AS-COLOCROSSING
Network
AS36352 AS-COLOCROSSING
WHOIS Information
NetRange
104.168.117.0 - 104.168.117.255
CIDR
104.168.117.0/24
NetName
CC-104-168-117-0-24
NetHandle
NET-104-168-117-0-1
Parent
CC-18 (NET-104-168-0-0-1)
NetType
Reassigned
OriginAS
Organization
RackNerd LLC (RL-872)
RegDate
2021-10-20
Updated
2022-03-02
Comment
Report abuse to: reportabuse@racknerd.com
Ref
https://rdap.arin.net/registry/entity/RL-872
OrgName
RackNerd LLC
OrgId
RL-872
Address
10602 N. Trademark Pkwy Suite 511
City
Rancho Cucamonga
StateProv
CA
PostalCode
91730
Country
US
OrgTechHandle
RACKN3-ARIN
OrgTechName
RackNerd NOC
OrgTechPhone
+1-888-881-6373
OrgTechEmail
support@racknerd.com
OrgTechRef
https://rdap.arin.net/registry/entity/RACKN3-ARIN
Attack Logs
Date Target Location Protocol Link
2025-08-04 Vultrparis SSH View Log

  • Country: United States
  • Network:
  • Noticed: 3 times
  • Protocols Attacked: ssh
  • Countries Attacked: Australia
Disclaimer
This page contains threat intelligence information for the IPv4 address 104.168.117.131 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.