102.218.210.100 Threat Intelligence - Kenya | IP Address Lookup

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General

IP Address
102.218.210.100
IPv4 Address
Location
🇰🇪 Kenya
KE
Network
AS328906
Directcore Technologies Limited
Threat Score
35/100
Medium Risk
Aggressive-DetectionbruteforceBruteforceBrute-ForceConnection-Resetdigital oceanportscanProtocol-Probing
Attack Intelligence
Noticed
10 times
Protocols Attacked
combined portscan ssh
Passive DNS
mail.directcore.com
Open Ports Detected
110143253894434655877071993995
Geographic Location
Country
Kenya
City
Unknown
Region
Unknown
Coordinates
1.0000, 38.0000
Network Information
ASN
AS328906
Organization
Directcore Technologies Limited
Network
AS328906 Directcore Technologies Limited
WHOIS Information
inetnum
102.218.208.0 - 102.218.211.255
netname
DTLv4
descr
Directcore Technologies Limited
country
KE
org
ORG-DTL4-AFRINIC
admin-c
BC25-AFRINIC
tech-c
TS59-AFRINIC
status
ALLOCATED PA
mnt-by
AFRINIC-HM-MNT
mnt-lower
DTL4-MNT
mnt-domains
DTL4-MNT
parent
102.0.0.0 - 102.255.255.255
organisation
ORG-DTL4-AFRINIC
org-name
Directcore Technologies Limited
org-type
LIR
address
No. 3, Sri-Aurobindo Avenue, Mzima Springs, Lavington, Nairobi - Kenya
phone
tel:+254-730-320111
mnt-ref
AFRINIC-HM-MNT
person
Betty Claudiah
nic-hdl
BC25-AFRINIC
Attack Logs
DateTarget LocationProtocolLink
2026-09-03 Digitaloceantoronto Combined Multiple View Log
2026-09-03 Toronto, Canada SSH View Log
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 102.218.210.100 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.