
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst 10/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 15/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 16/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 20/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 24/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 28/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 36/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 8/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 9/2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 24 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 27 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 28 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 3 | Medium-risk: admin panels, config files | +30 | |
| Danger medium hits: 5 | Medium-risk: admin panels, config files | +50 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Danger strong hits: 4 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
We have detected 2 malicious IPs from this autonomous system.
IP 130.49.215.84 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
IP 130.49.215.84 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Address UA spoofing from 130.49.215.84: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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.
130.49.215.84 has been assigned a threat score of 300/100 (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:
130.49.215.84 is registered in Warsaw, Poland, operating on the network of Shesternin Vladimir Anatolievich. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Over a period of 20 days, this IP generated 544 malicious requests, averaging approximately 27.2 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. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. Our records show 591 malicious IPs originating from Poland, positioning it as a major contributor to global threat activity. At 300/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.