IP Threat Report
20.203.247.7
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 | |
| Danger strong hits: 36 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 351 | Medium-risk: admin panels, config files | +60 | |
| 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: 45 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 132 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 50 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Burst: 63 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 200 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 64 req / 2s | Abnormally fast request rate — automated scanning | +35 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Network Provider
Recommendations
Actions taken & recommended
- IP 20.203.247.7 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🤖 User-Agent Anomaly Defense
IP 20.203.247.7 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
🔎 Path Enumeration Protection
Block scanning from 20.203.247.7: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 20.203.247.7.
Blacklist Status (DNSBL)
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.
Threat Analysis
20.203.247.7 has been assigned a threat score of 280/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The following attack categories were identified:
📊 Threat Analysis
The address 20.203.247.7 originates from Zurich, Switzerland, operating on the network of Microsoft Corporation. It was identified through automated analysis of incoming network traffic across monitored endpoints. Our sensors captured 3 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~3 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. Our records show 102 malicious IPs originating from Switzerland, positioning it as a significant contributor to global threat activity. 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.
Related Threats
🇨🇭 Top threats from Switzerland
51.107.69.151 (280)20.250.10.152 (280)20.203.180.135 (280)20.203.136.132 (280)20.203.129.217 (280)View all →Security Intelligence
💡 User-Agent Analysis Techniques
Analyzing User-Agent strings reveals automated tools masquerading as legitimate browsers. Inconsistencies between claimed browser capabilities and actual behavior, impossible version combinations, and known scanner signatures help identify malicious clients.
💡 Machine Learning in Threat Detection
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.