IP Threat Report
194.5.82.20
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: 12 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 43 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| Danger strong hits: 101 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| UA changed for same IP | 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 194.5.82.20 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 194.5.82.0/24
- 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 194.5.82.20: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🌊 Traffic Flood Defense
IP 194.5.82.20 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
🤖 User-Agent Anomaly Defense
IP 194.5.82.20 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Neighbors in 194.5.82.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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
194.5.82.20 has been assigned a threat score of 215/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
The following attack categories were identified:
📊 Threat Analysis
Threat intelligence analysis has linked 194.5.82.20 to malicious activity originating from Singapore, Singapore, operating on the network of GSL Networks Pty LTD. The address has been under observation since its initial detection. Our sensors captured 917 malicious requests from this address across a 60-day span, reflecting a sustained attack cadence of ~15.3 requests per day. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. With 157 flagged addresses, Singapore represents a significant presence in our threat database. A score of 215/100 places this address in the top tier of severity. Block and investigate any historical connections.
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.