203.202.252.102 Threat Intelligence - Bangladesh | IP Address Lookup

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General

IP Address
203.202.252.102
IPv4 Address
Location
🇧🇩 Savar Upazila, Bangladesh
BD
Network
AS24323
aamra networks limited,
Threat Score
40/100
Medium Risk
cyber securityiocmaliciousNextrayphishing
Attack Intelligence
Noticed
29 times
Protocols Attacked
Anonymous Proxy
Countries Attacked
Canada, Czechia, Denmark, Estonia, France, Germany, Latvia, Lithuania, Norway, Poland, Romania, Turkey, Ukraine, United Kingdom of Great Britain and Northern Ireland, United States of America
Passive DNS
www.lplmis.com
Geographic Location
Country
Bangladesh
City
Savar Upazila
Region
Dhaka Division
Coordinates
23.8468, 90.2417
Network Information
ASN
AS24323
Organization
aamra networks limited,
Network
AS24323 aamra networks limited,
WHOIS Information
inetnum
203.202.240.0 - 203.202.255.255
netname
AAMRA-AS-AP
descr
Aamra Networks Limited, Internet Service Provider.
country
BD
org
ORG-ANL12-AP
admin-c
SA119-AP
tech-c
MR240-AP
abuse-c
AA1900-AP
status
ALLOCATED PORTABLE
mnt-by
APNIC-HM
mnt-lower
MAINT-BD-AAMRA
mnt-routes
MAINT-BD-AAMRA
mnt-irt
IRT-AAMRA-BD
last-modified
2020-11-25T06:35:49Z
irt
IRT-AAMRA-BD
address
Safura Tower (12th floor),
e-mail
abuse@aamra.com.bd
abuse-mailbox
abuse@aamra.com.bd
organisation
ORG-ANL12-AP
org-name
Aamra Networks Limited
org-type
LIR
phone
+880-2-8861100
fax-no
+880-2-8860044
mnt-ref
APNIC-HM
Attack Logs
DateTarget LocationProtocolLink
2026-04-14 Perth, Australia Proxy View Log
Additional Intelligence
Threat Feeds / IP Sets
  • proxyspy_30d
  • proxyspy_7d
Disclaimer
This page contains threat intelligence information for the IPv4 address 203.202.252.102 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.