IP Threat Report
180.93.0.112
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 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst 21/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 22/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 30/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 5/2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 18 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Danger strong hits: 12 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger strong hits: 30 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| 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 180.93.0.112 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 180.93.0.112: 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 180.93.0.112.
🤖 User-Agent Anomaly Defense
IP 180.93.0.112 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
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
180.93.0.112 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
Threat intelligence analysis has linked 180.93.0.112 to malicious activity originating from Quận Bốn, Vietnam, operating on the network of SPT. The address has been under observation since its initial detection. The address has been active for 36 days in our monitoring system, producing 498 flagged requests at a rate of ~13.8/day. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. With 124 flagged addresses, Vietnam represents a significant presence in our threat database. 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 is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
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💡 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.