IP Threat Report
35.200.81.158
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst 169/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 67/2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 7 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 30 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| POST seen | Behavioral anomaly detected by automated analysis | +8 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 |
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 35.200.81.158 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
🔎 Path Enumeration Protection
Block scanning from 35.200.81.158: 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 35.200.81.158.
🤖 Bot Detection
Address UA spoofing from 35.200.81.158: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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
35.200.81.158 has been assigned a threat score of 298/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
📊 Threat Analysis
The address 35.200.81.158 originates from Tokyo, Japan, operating on the network of Google LLC. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 1 days in our monitoring system, producing 33 flagged requests at a rate of ~33/day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. With 3 different attack patterns detected, this IP exhibits behavior characteristic of advanced automated scanning frameworks. With 104 flagged addresses, Japan represents a significant presence in our threat database. With a threat score of 298/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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
Security Intelligence
💡 DDoS Mitigation Approaches
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.
💡 Web Application Firewall Strategies
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.