
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 10 | Medium-risk: admin panels, config files | +60 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Foreign referer seen | Referer from unrelated external domain | +10 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 198.245.71.168 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 80 | HTTP | Low | HTTP web server — standard web traffic |
| 3128 | Unknown | Low | Service on port 3128 |
| 8000 | Unknown | Low | Service on port 8000 |
| 8800 | Unknown | Low | Service on port 8800 |
| 52931 | Unknown | Low | Service on port 52931 |
| CVE ID | Link |
|---|---|
| CVE-2019-12524 | NVD → |
| CVE-2022-41318 | NVD → |
| CVE-2019-12521 | NVD → |
| CVE-2019-18677 | NVD → |
| CVE-2020-11945 | NVD → |
| CVE-2020-8449 | NVD → |
| CVE-2024-45802 | NVD → |
| CVE-2025-62168 | NVD → |
| CVE-2023-5824 | NVD → |
| CVE-2023-46846 | NVD → |
| CVE-2020-15810 | NVD → |
| CVE-2018-1000027 | NVD → |
| CVE-2021-28652 | NVD → |
| CVE-2023-46847 | NVD → |
| CVE-2020-15049 | NVD → |
| CVE-2020-24606 | NVD → |
| CVE-2020-8517 | NVD → |
| CVE-2025-54574 | NVD → |
| CVE-2021-31807 | NVD → |
| CVE-2021-31806 | NVD → |
| CVE-2021-31808 | NVD → |
| CVE-2018-1000024 | NVD → |
| CVE-2023-50269 | NVD → |
| CVE-2021-28116 | NVD → |
| CVE-2019-12529 | NVD → |
🔴 This host has 56 known CVEs associated with its exposed services. This volume strongly suggests severely outdated software. Review each CVE in the NVD database.
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
198.245.71.168 has been assigned a threat score of 105/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The following attack categories were identified:
The address 198.245.71.168 originates from Buffalo, United States, operating on the network of B2 Net Solutions Inc.. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 requests per day. The IP is classified as hosting/datacenter infrastructure, commonly associated with rented servers used for automated attack campaigns, botnet command-and-control, or vulnerability scanning at scale. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. United States currently accounts for 178 blocked IPs in our database, making it a significant source of malicious traffic. A score of 105/100 places this address in the top tier of severity. Block and investigate any historical connections.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
Brute force attacks systematically try username and password combinations to gain unauthorized access. Modern attacks leverage credential databases from previous breaches, testing millions of combinations using distributed botnets across multiple IP addresses.
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.