
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: node-fetch | Known bot/crawler User-Agent detected | +40 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +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 217.116.58.107 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
217.116.58.107 has been assigned a threat score of 50/100 (Medium). The address carries a moderate risk rating. Defensive monitoring is advised, with escalation to blocking if activity intensifies.
The following attack categories were identified:
The address 217.116.58.107 originates from Tyumen, Russia, operating on the network of Russian company LLC. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. Detected suspicious User-Agent anomalies including empty, forged, or rapidly rotating UA strings — characteristic of automated scanning tools. With 122 flagged addresses, Russia represents a significant presence in our threat database. The score of 50/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
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.
Effective rate limiting must balance protection against abuse with allowing legitimate traffic bursts. Sliding window algorithms, token buckets, and adaptive thresholds based on client reputation provide layered defense against flooding attacks.