141.98.10.205 Threat Intelligence - Lithuania | IP Address Lookup

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General

IP Address
141.98.10.205
IPv4 Address
Location
🇱🇹 Lithuania
LT
Network
AS209605
UAB Host Baltic
Threat Score
50/100
Medium Risk
abuseadbandroidautomatedblacklistblock listBlocklistbotnet
Attack Intelligence
MITRE ATT&CK Techniques
T1059 - Command and Scripting Interpreter, T1105 - Ingress Tool Transfer, T1190 - Exploit Public-Facing Application, T1595 - Active Scanning
Noticed
29 times
Protocols Attacked
combined portscan
Countries Attacked
Australia, China, Germany, Russian Federation, United Kingdom of Great Britain and Northern Ireland, United States of America
Open Ports Detected
22
Geographic Location
Country
Lithuania
City
Unknown
Region
Unknown
Coordinates
55.4167, 24.0000
Network Information
ASN
AS209605
Organization
UAB Host Baltic
Network
AS209605 UAB Host Baltic
WHOIS Information
inetnum
141.98.10.0 - 141.98.10.255
netname
LT-HOSTBALTIC-10
country
LT
admin-c
PV7242-RIPE
tech-c
PV7242-RIPE
status
ASSIGNED PA
mnt-by
mnt-lt-hostbaltic-1
created
2019-01-10T13:11:38Z
last-modified
2019-01-10T13:11:38Z
person
Paulius Vancugovas
address
Draugystes g. 19
phone
+37067358624
nic-hdl
PV7242-RIPE
route
141.98.10.0/24
origin
AS209605
Attack Logs
DateTarget LocationProtocolLink
2026-08-23 Digitaloceantoronto Combined Multiple View Log
2026-08-22 Digitaloceantoronto Combined Multiple View Log
2026-08-21 Digitaloceantoronto Combined Multiple View Log
2026-08-20 Digitaloceantoronto Combined Multiple View Log
2026-08-19 Digitaloceantoronto Combined Multiple View Log
2026-08-17 Digitaloceantoronto Combined Multiple View Log
Disclaimer
This page contains threat intelligence information for the IPv4 address 141.98.10.205 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.