IP Threat Report
159.223.36.188
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 11/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 24 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 36 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger strong hits: 4 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 |
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
We have detected 4068 malicious IPs from this autonomous system.
Recommendations
Actions taken & recommended
- IP 159.223.36.188 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 159.223.36.0/24
- 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 159.223.36.188 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 159.223.36.188.
Neighbors in 159.223.36.0/24
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
159.223.36.188 has been assigned a threat score of 205/100 (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 159.223.36.188 originates from Singapore, Singapore, operating on the network of DigitalOcean, LLC. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 4-day observation window, we recorded 358 hostile requests from this IP — roughly 89.5 per day on average. 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. Two attack patterns were identified (Path Enumeration and Request Flooding), suggesting a semi-automated campaign that targets multiple vulnerabilities. With 5,128 flagged addresses, Singapore represents a major presence in our threat database. A score of 205/100 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.