172.104.240.26 Threat Intelligence - Germany | IP Address Lookup
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General
IP Address
172.104.240.26
Location
🇩🇪 Frankfurt am Main, Germany
Network
AS63949
Threat Score
56/100
Attack Intelligence
MITRE ATT&CK Techniques
T1059 - Command and Scripting Interpreter, T1105 - Ingress Tool Transfer, T1190 - Exploit Public-Facing Application, T1595 - Active Scanning
Noticed
4 times
Protocols Attacked
combined portscan
Passive DNS
intern.update-ukt.services
Open Ports Detected
2250022808080
Geographic Location
Country
Germany
City
Frankfurt am Main
Region
Hesse
Coordinates
50.1169, 8.6837
Network Information
ASN
AS63949
Organization
Akamai Connected Cloud
Network
AS63949 Akamai Connected Cloud
Associated CVEs
WHOIS Information
NetRange
172.104.0.0 - 172.105.255.255
CIDR
172.104.0.0/15
NetName
LINODE-US
NetHandle
NET-172-104-0-0-1
Parent
NET172 (NET-172-0-0-0-0)
NetType
Direct Allocation
Organization
Akamai Technologies, Inc. (AKAMAI)
RegDate
2015-06-19
Updated
2023-09-18
Comment
Geofeed https://ipgeo.akamai.com/linode-geofeed.csv
Ref
https://rdap.arin.net/registry/ip/172.104.0.0
OrgName
Akamai Technologies, Inc.
OrgId
AKAMAI
Address
145 Broadway
City
Cambridge
StateProv
MA
PostalCode
02142
Country
US
OrgTechHandle
IPADM11-ARIN
OrgTechName
ipadmin
OrgTechPhone
+1-617-444-0017
OrgTechEmail
ip-admin@akamai.com
OrgTechRef
https://rdap.arin.net/registry/entity/IPADM11-ARIN
OrgAbuseHandle
NUS-ARIN
Attack Logs
| Date | Target Location | Protocol | Link |
|---|---|---|---|
| 2026-08-20 | Digitaloceantoronto Combined | Multiple | View Log |
| 2026-08-18 | Digitaloceantoronto Combined | Multiple | View Log |
Disclaimer
This page contains threat intelligence information for the IPv4 address 172.104.240.26 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.