161.97.132.154 Threat Intelligence and Host Information

General

IP Address
161.97.132.154
IPv4 Address
Location
🇩🇪 Düsseldorf, Germany
DE
Network
AS51167
Contabo GmbH
Threat Score
38/100
Medium Risk
BlocklistcowrieMalicious-IPportscanresearchscannerScannerscanners
Attack Intelligence
Open Ports Detected
2083
Geographic Location
Country
Germany
City
Düsseldorf
Region
North Rhine-Westphalia
Coordinates
51.1878, 6.8607
Network Information
ASN
AS51167
Organization
Contabo GmbH
Network
AS51167 Contabo GmbH
WHOIS Information
NetRange
161.97.64.0 - 161.97.189.255
CIDR
161.97.184.0/22, 161.97.188.0/23, 161.97.64.0/18, 161.97.128.0/19, 161.97.176.0/21, 161.97.160.0/20
NetName
RIPE
NetHandle
NET-161-97-64-0-1
Parent
NET161 (NET-161-0-0-0-0)
NetType
Early Registrations, Transferred to RIPE NCC
OriginAS
Organization
RIPE Network Coordination Centre (RIPE)
RegDate
Updated
2013-07-29
Ref
https://rdap.arin.net/registry/entity/RIPE
OrgName
RIPE Network Coordination Centre
OrgId
RIPE
Address
P.O. Box 10096
City
Amsterdam
StateProv
PostalCode
1001EB
Country
NL
OrgTechHandle
RNO29-ARIN
OrgTechName
RIPE NCC Operations
OrgTechPhone
+31 20 535 4444
OrgTechEmail
hostmaster@ripe.net
OrgTechRef
https://rdap.arin.net/registry/entity/RNO29-ARIN
OrgAbuseHandle
ABUSE3850-ARIN

  • Country: Germany
  • Network:
  • Noticed: 4 times
  • Protocols Attacked: portscan
  • Countries Attacked: China, Germany, Russian Federation, United Kingdom of Great Britain and Northern Ireland, United States of America

Malware Detected on Host

Count:

CVEs Detected

CVE-2007-3205 CVE-2013-2220 CVE-2022-4900 CVE-2024-25117 CVE-2024-3566 CVE-2024-5458

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Disclaimer
This page contains threat intelligence information for the IPv4 address 161.97.132.154 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.