IP Threat Report
20.89.242.68
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst 12/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 42/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 43/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 273 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 410 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 45 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 25 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 35 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 4 | 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 |
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 20.89.242.68 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
🔎 Directory Scan Defense
IP 20.89.242.68 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 20.89.242.68.
🤖 Bot Detection
Address UA spoofing from 20.89.242.68: 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
20.89.242.68 has been assigned a threat score of 280/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
📊 Threat Analysis
The address 20.89.242.68 originates from Osaka, Japan, operating on the network of Microsoft Corporation. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 4 days, this IP generated 736 malicious requests, averaging approximately 184 requests per day. The IP is classified as hosting/datacenter infrastructure, commonly associated with rented servers used for automated attack campaigns, botnet command-and-control, or vulnerability scanning at scale. The combination of 3 distinct attack vectors indicates a sophisticated, multi-pronged threat actor deploying automated tools that probe multiple attack surfaces simultaneously. Our records show 106 malicious IPs originating from Japan, positioning it as a significant contributor to global threat activity. With a threat score of 280/100, 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.
💡 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.