162.216.16.13 Threat Intelligence and Host Information
Apr 06, 2026
ipinfopage
General
IP Address
162.216.16.13
Location
🇺🇸 Cedar Knolls, United States
Network
AS63949
Threat Score
29/100
Attack Intelligence
Open Ports Detected
80
Geographic Location
Country
United States
City
Cedar Knolls
Region
New Jersey
Coordinates
40.8229, -74.4592
Network Information
ASN
AS63949
Organization
Linode, LLC
Network
AS63949 Linode, LLC
WHOIS Information
NetRange
162.216.16.0 - 162.216.19.255
CIDR
162.216.16.0/22
NetName
LINODE
NetHandle
NET-162-216-16-0-2
Parent
LINODE-US (NET-162-216-16-0-1)
NetType
Reassigned
OriginAS
Organization
Linode (LINOD)
RegDate
2008-04-24
Updated
2022-12-15
Comment
http://www.linode.com
Ref
https://rdap.arin.net/registry/entity/LINOD
OrgName
Linode
OrgId
LINOD
Address
249 Arch St
City
Philadelphia
StateProv
PA
PostalCode
19106
Country
US
OrgTechHandle
LNO21-ARIN
OrgTechName
Linode Network Operations
OrgTechPhone
+1-609-380-7100
OrgTechEmail
support@linode.com
OrgTechRef
https://rdap.arin.net/registry/entity/LNO21-ARIN
- Country: United States
- Network:
- Noticed: 2 times
- Protocols Attacked: portscan
- Passive DNS Results: predim.pacsninja.com www.pacsninja.com ris.pacsninja.com santaisabel.pacsninja.com pacsninja.com ns2.ilibris.be
CVEs Detected
CVE-2007-4723 CVE-2009-0796 CVE-2009-2299 CVE-2011-1176 CVE-2011-2688 CVE-2012-3526 CVE-2012-4001 CVE-2012-4360 CVE-2013-0941 CVE-2013-0942 CVE-2013-2765 CVE-2013-4365 CVE-2024-42516 CVE-2024-43204 CVE-2024-43394 CVE-2024-47252 CVE-2025-23048 CVE-2025-49630 CVE-2025-49812 CVE-2025-53020 CVE-2025-55753 CVE-2025-58098 CVE-2025-59775 CVE-2025-65082 CVE-2025-66200
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This page contains threat intelligence information for the IPv4 address 162.216.16.13 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.