
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst 18/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 18/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 23/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 24/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 27/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 36/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 18 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 27 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 45 | Medium-risk: admin panels, config files | +60 | |
| 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: 7 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| UA suspicious | Behavioral anomaly detected by automated analysis | +15 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 20.151.214.118 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 20.151.214.118.
Address UA spoofing from 20.151.214.118: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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.
20.151.214.118 has been assigned a threat score of 245/100 (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:
Our monitoring infrastructure has identified 20.151.214.118, geolocated to Toronto, Canada, operating on the network of Microsoft Corporation, as a source of suspicious network activity. During its 3-day observation window, we recorded 1,393 hostile requests from this IP — roughly 464.3 per day on average. Classified as a hosting IP, this address likely runs on a rented server or cloud instance. Attackers prefer datacenter IPs for their high bandwidth and disposable nature. The combination of 3 distinct attack vectors indicates a sophisticated, multi-pronged threat actor deploying automated tools that probe multiple attack surfaces simultaneously. Canada currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. At 245/100, 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.
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.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.