IP Threat Report
20.89.105.191
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 11/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 12/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 38/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 41/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 42/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 11 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 12 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 38 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 41 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 42 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 40 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 11 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 13 | High-risk paths: shells, RCE vectors, exploits | +100 |
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 20.89.105.191 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 20.89.105.191: 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 20.89.105.191.
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
20.89.105.191 has been assigned a threat score of 255/100 (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
Our monitoring infrastructure has identified 20.89.105.191, geolocated to Tokyo, Japan, operating on the network of Microsoft Corporation, as a source of suspicious network activity. Our sensors captured 641 malicious requests from this address across a 6-day span, reflecting a sustained attack cadence of ~106.8 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. Two attack patterns were identified (Path Enumeration and Request Flooding), suggesting a semi-automated campaign that targets multiple vulnerabilities. Our records show 101 malicious IPs originating from Japan, positioning it as a significant contributor to global threat activity. A score of 255/100 places this address in the top tier of severity. Block and investigate any historical connections.
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.
💡 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.