
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst: 7 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 14 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Burst: 5 req / 2s | Abnormally fast request rate — automated scanning | +35 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 101.132.184.223.
Block scanning from 101.132.184.223: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
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 |
| 80 | HTTP | Low | HTTP web server — standard web traffic |
| 443 | HTTPS | Low | HTTPS web server — encrypted web traffic |
| 3306 | MySQL | High | MySQL database — should never be exposed to the internet |
⚠️ 1 high-risk port detected on 101.132.184.223. These services should not be publicly accessible without strict firewall rules.
| CVE ID | Link |
|---|---|
| CVE-2019-16905 | NVD → |
| CVE-2020-15778 | NVD → |
| CVE-2023-48795 | NVD → |
| CVE-2023-38408 | NVD → |
| CVE-2020-14145 | NVD → |
| CVE-2025-32728 | NVD → |
| CVE-2023-51767 | NVD → |
| CVE-2007-2768 | NVD → |
| CVE-2008-3844 | NVD → |
| CVE-2021-41617 | NVD → |
| CVE-2025-26465 | NVD → |
| CVE-2023-51385 | NVD → |
| CVE-2021-36368 | NVD → |
| CVE-2016-20012 | NVD → |
🔴 This host has 14 known CVEs associated with its exposed services. This volume strongly suggests severely outdated software. Review each CVE in the NVD database.
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
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.
101.132.184.223 has been assigned a threat score of 90/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The following attack categories were identified:
Network traffic from 101.132.184.223, located in Shanghai, China, operating on the network of Hangzhou Alibaba Advertising Co, has been classified as malicious by our automated threat scoring engine. The address has been active for 1 days in our monitoring system, producing 2 flagged requests at a rate of ~2/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 dual attack vectors of Request Flooding combined with Path Enumeration indicate a coordinated assault rather than opportunistic scanning. China currently accounts for 123 blocked IPs in our database, making it a significant source of malicious traffic. At 90/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
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 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.