
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: curl | Known bot/crawler User-Agent detected | +40 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Address UA spoofing from 92.63.194.174: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
IP 92.63.194.174 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 2211 | Unknown | Low | Service on port 2211 |
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.
92.63.194.174 has been assigned a threat score of 75/100 (High). This score indicates high threat severity. The IP has shown clear patterns of malicious behavior that warrant immediate defensive measures.
The following attack categories were identified:
IP address 92.63.194.174 has been traced to Novoivanovskoye, Russia, operating on the network of JSC IOT. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Over a period of 1 days, this IP generated 2 malicious requests, averaging approximately 2 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. Two attack patterns were identified (User-Agent Anomaly and Path Enumeration), suggesting a semi-automated campaign that targets multiple vulnerabilities. With 111 flagged addresses, Russia represents a significant presence in our threat database. At 75/100, this IP warrants immediate defensive action.
This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
TLS fingerprinting creates unique identifiers based on how clients negotiate encrypted connections. The JA3 and JA4 methods generate hashes from TLS ClientHello parameters, enabling identification of specific tools and malware regardless of IP address changes.
Artificial intelligence enables more convincing phishing content, faster vulnerability discovery, and adaptive attack strategies that learn from defensive responses. AI-generated social engineering and automated exploit development represent growing threats.