
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 212 | Medium-risk: admin panels, config files | +60 | |
| Burst: 21 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 75 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Burst: 22 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 74 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 73 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 69 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 23 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 76 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 77 req / 10s | Abnormally fast request rate — automated scanning | +35 |
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 20.151.107.165: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 20.151.107.165.
IP 20.151.107.165 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.
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.
20.151.107.165 has been assigned a threat score of 280/100 (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:
20.151.107.165 is registered in Toronto, Canada, operating on the network of Microsoft Corporation. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Over a period of 1 days, this IP generated 13 malicious requests, averaging approximately 13 requests per day. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. With 3 different attack patterns detected, this IP exhibits behavior characteristic of advanced automated scanning frameworks. Canada currently accounts for 121 blocked IPs in our database, making it a significant source of malicious traffic. At 280/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
CAPTCHAs remain a primary bot defense but face increasing bypass rates from AI-powered solvers. Modern alternatives include invisible behavioral analysis, proof-of-work challenges, and device fingerprinting that detect bots without impacting user experience.