IP Threat Report
47.238.239.183
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst: 5 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 12 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer seen | Referer from unrelated external domain | +10 |
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 47.238.239.183 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
🌊 Traffic Flood Defense
IP 47.238.239.183 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Open Ports & Services
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 22 | SSH | Low | Secure Shell — common brute force target for remote access |
| CVE ID | Link |
|---|---|
| CVE-2018-15473 | NVD → |
| CVE-2021-36368 | NVD → |
| CVE-2025-32728 | NVD → |
| CVE-2025-26465 | NVD → |
| CVE-2019-6109 | NVD → |
| CVE-2019-6111 | NVD → |
| CVE-2017-15906 | NVD → |
| CVE-2023-48795 | NVD → |
| CVE-2019-6110 | NVD → |
| CVE-2020-15778 | NVD → |
| CVE-2021-41617 | NVD → |
| CVE-2023-38408 | NVD → |
| CVE-2026-35414 | NVD → |
| CVE-2008-3844 | NVD → |
| CVE-2016-20012 | NVD → |
| CVE-2023-51767 | NVD → |
| CVE-2007-2768 | NVD → |
| CVE-2023-51385 | NVD → |
| CVE-2018-20685 | NVD → |
| CVE-2018-15919 | NVD → |
| CVE-2020-14145 | NVD → |
🔴 Security scanning identified 21 vulnerability entries on this host. This volume strongly suggests severely outdated software. Consult NVD advisories for details.
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
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
47.238.239.183 has been assigned a threat score of 80/100 (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
Our monitoring infrastructure has identified 47.238.239.183, geolocated to Hong Kong, Hong Kong, operating on the network of Alibaba Cloud LLC, as a source of suspicious network activity. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 requests per day. Classified as a hosting IP, this address likely runs on a rented server or cloud instance. Attackers prefer datacenter IPs for their high bandwidth and disposable nature. The IP is engaged in request flooding, sending traffic at rates designed to exhaust server capacity. Our records show 190 malicious IPs originating from Hong Kong, positioning it as a significant contributor to global threat activity. The score of 80/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
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.
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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.