179.108.86.242 Threat Intelligence and Host Information

General

IP Address
179.108.86.242
IPv4 Address
Location
🇧🇷 Campinas, Brazil
BR
Network
AS53158
Net Turbo Telecom
Threat Score
45/100
Medium Risk
cowriedigitaloceandionaeafatthoneytrapmailoneyp0f
Attack Intelligence
Open Ports Detected
10001
Geographic Location
Country
Brazil
City
Campinas
Region
Sao Paulo
Coordinates
-22.8950, -47.0439
Network Information
ASN
AS53158
Organization
Net Turbo Telecom
Network
AS53158 Net Turbo Telecom
WHOIS Information
inetnum
179.108.80.0/21
aut-num
AS53158
abuse-c
GANTU3
owner
Net Turbo Telecom
ownerid
32.799.248/0001-50
responsible
Net Turbo Telecom
country
BR
owner-c
CALGA41
tech-c
NOCNT
inetrev
179.108.86.0/24
nserver
ns-438.awsdns-54.com
nsstat
20260425 AA
nslastaa
20260425
created
20110822
changed
20251128
nic-hdl-br
NOCNT
person
Network Operation Center Net Turbo
e-mail
noc@netturbo.com.br

  • Country: Brazil
  • Network:
  • Noticed: 5 times
  • Protocols Attacked: portscan

CVEs Detected

CVE-2007-2768 CVE-2008-3844 CVE-2012-6708 CVE-2014-2532 CVE-2014-2653 CVE-2015-5352 CVE-2015-5600 CVE-2015-6563 CVE-2015-6564 CVE-2015-9251 CVE-2016-0777 CVE-2016-0778 CVE-2016-10009 CVE-2016-10010 CVE-2016-10011 CVE-2016-10012 CVE-2016-10708 CVE-2016-1908 CVE-2016-20012 CVE-2016-3115 CVE-2017-15906 CVE-2018-15473 CVE-2018-15919 CVE-2018-20685 CVE-2019-11358 CVE-2019-6109 CVE-2019-6110 CVE-2019-6111 CVE-2020-11022 CVE-2020-11023 CVE-2020-14145 CVE-2020-15778 CVE-2020-7656 CVE-2021-36368 CVE-2021-41617 CVE-2023-38408 CVE-2023-48795 CVE-2023-51385 CVE-2023-51767 CVE-2026-35414

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Disclaimer
This page contains threat intelligence information for the IPv4 address 179.108.86.242 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.