173.246.88.3 Threat Intelligence - Canada | IP Address Lookup

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General

IP Address
173.246.88.3
IPv4 Address
Location
🇨🇦 Saint-Laurent, Canada
CA
Network
AS40191
Beanfield Technologies Inc.
Threat Score
37/100
Medium Risk
Aggressive-DetectionbruteforceBruteforceBrute-ForceConnection-Resetdigital oceanportscanProtocol-Probing
Attack Intelligence
Noticed
7 times
Protocols Attacked
combined portscan ssh
Open Ports Detected
25443
Geographic Location
Country
Canada
City
Saint-Laurent
Region
Quebec
Coordinates
45.5191, -73.6852
Network Information
ASN
AS40191
Organization
Beanfield Technologies Inc.
Network
AS40191 Beanfield Technologies Inc.
WHOIS Information
NetRange
173.246.64.0 - 173.246.95.255
CIDR
173.246.64.0/19
NetName
BEANFIELD-MTL-06
NetHandle
NET-173-246-64-0-1
Parent
NET173 (NET-173-0-0-0-0)
NetType
Direct Allocation
Organization
Beanfield Technologies Inc. (BNFD)
RegDate
2010-06-16
Updated
2025-01-15
Comment
Geofeed https://geofeed.beanfield.com/geofeed.csv
Ref
https://rdap.arin.net/registry/ip/173.246.64.0
OrgName
Beanfield Technologies Inc.
OrgId
BNFD
City
Toronto
StateProv
ON
PostalCode
M6K-3E3
Country
CA
OrgAbuseHandle
BSAO-ARIN
OrgAbuseName
Beanfield security and abuse officer
OrgAbusePhone
+1-416-532-1555
OrgAbuseEmail
abuse@beanfield.com
OrgAbuseRef
https://rdap.arin.net/registry/entity/BSAO-ARIN
OrgTechHandle
BEANF1-ARIN
OrgTechName
Beanfield Technologies Inc
Attack Logs
DateTarget LocationProtocolLink
2026-09-02 Digitaloceantoronto Combined Multiple View Log
2026-09-02 Toronto, Canada SSH View Log
2026-09-01 Toronto, Canada SSH View Log
2026-09-01 Digitaloceantoronto Combined Multiple View Log
Disclaimer
This page contains threat intelligence information for the IPv4 address 173.246.88.3 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.