103.3.61.10 Threat Intelligence and Host Information

General

IP Address
103.3.61.10
IPv4 Address
Location
🇸🇬 Singapore, Singapore
SG
Network
AS63949
Linode, LLC
Threat Score
55/100
High Risk
Brute-ForceBruteforceSSHcowriessh
Attack Intelligence
MITRE ATT&CK Techniques
T1078 - Valid Accounts, T1083 - File and Directory Discovery, T1098.004 - SSH Authorized Keys, T1105 - Ingress Tool Transfer, T1110 - Brute Force, T1110.004 - Credential Stuffing
Open Ports Detected
22
Geographic Location
Country
Singapore
City
Singapore
Region
Unknown
Coordinates
1.3036, 103.8554
Network Information
ASN
AS63949
Organization
Linode, LLC
Network
AS63949 Linode, LLC
WHOIS Information
inetnum
103.3.60.0 - 103.3.61.255
netname
LINODE-AP
descr
Akamai Connected Cloud / Linode
country
US
admin-c
LLNA1-AP
tech-c
LLNA1-AP
abuse-c
AL1444-AP
status
ALLOCATED NON-PORTABLE
mnt-by
MAINT-AP-AKAMAI
mnt-irt
IRT-LINODE-AP
last-modified
2023-02-21T14:19:33Z
irt
IRT-LINODE-AP
address
329 E. Jimmie Leeds Road, Suite A, Galloway, NJ 08205 USA
e-mail
ip-admin@akamai.com
abuse-mailbox
abuse@linode.com
role
LINODE LLC - network administrator
phone
+1-609-380-7100
nic-hdl
LLNA1-AP
fax-no
+1-609-380-7200
route
103.3.61.0/24
origin
AS63949
Attack Logs
Date Target Location Protocol Link
2023-02-06 Dobengaluru MSSQL View Log

CVEs Detected

CVE-2006-20001 CVE-2022-22719 CVE-2022-22720 CVE-2022-22721 CVE-2022-23943 CVE-2022-26377 CVE-2022-28330 CVE-2022-28614 CVE-2022-28615 CVE-2022-29404 CVE-2022-30556 CVE-2022-31813 CVE-2022-36760 CVE-2022-37436 CVE-2023-25690 CVE-2023-26048 CVE-2023-26049 CVE-2023-27522

Disclaimer
This page contains threat intelligence information for the IPv4 address 103.3.61.10 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.