
ABUSE.MOM — 规矩点,否则你将被曝光
| 签名 | 描述 | 分数 | 严重性 |
|---|---|---|---|
| UA bot: crawler | 检测到已知机器人/爬虫的User-Agent | +40 | |
| Danger medium hits: 1 | 中等风险:管理面板、配置文件 | +10 |
从服务器访问日志重建的HTTP请求。出于安全考虑,目标域名已隐藏。
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 38.38.253.211显示可疑的UA行为。阻止空User-Agent请求。为敏感端点实施基于JavaScript的机器人检测。
来自Shodan的网络侦察数据。开放端口可能表示正在运行的服务、错误配置或潜在的攻击面。
| Port | Service | Risk | Description |
|---|---|---|---|
| 22 | SSH | Low | Secure Shell — common brute force target for remote access |
| 2000 | Unknown | Low | Service on port 2000 |
| 2332 | Unknown | Low | Service on port 2332 |
| 5555 | Unknown | Low | Service on port 5555 |
| 6666 | Unknown | Low | Service on port 6666 |
| 6667 | Unknown | Low | Service on port 6667 |
| 7777 | Unknown | Low | Service on port 7777 |
| 7778 | Unknown | Low | Service on port 7778 |
| 8886 | Unknown | Low | Service on port 8886 |
| 8887 | Unknown | Low | Service on port 8887 |
| 8888 | HTTP-Alt | Low | Service on port 8888 |
| 8889 | Unknown | Low | Service on port 8889 |
| 8899 | Unknown | Low | Service on port 8899 |
| 9100 | Unknown | Low | Service on port 9100 |
| 10050 | Unknown | Low | Service on port 10050 |
数据来源:Shodan InternetDB。独立于abuse.mom进行扫描。
该IP已通过全球邮件服务器和防火墙使用的主要DNS黑名单进行检查。
已检查:Spamhaus、SpamCop、Barracuda、SORBS、CBL、UCEProtect。
38.38.253.211 has been assigned a threat score of 50/100 (Medium). 这是一个中等威胁。虽然不是最危险的,但该IP显示出需要监控和可能限速的模式。
The following attack categories were identified:
地址38.38.253.211来源于San Jose, United States,运营在ZENLAYER.INC的网络中。它是通过对受监控端点的入站网络流量进行自动分析而被识别的。 在其1天的观察窗口期间,我们记录了来自此IP的1次敌对请求——平均每天约1次。 该地址被归类为住宅,意味着它可能属于终端用户ISP连接。来自住宅IP的恶意活动通常表明设备已被入侵或属于僵尸网络。 检测到可疑的User-Agent异常,包括空的、伪造的或快速轮换的UA字符串——自动化扫描工具的特征。 我们的记录显示来自United States的62个恶意IP,使其成为全球威胁活动的值得注意的贡献者。 评分50/100需要主动监控和速率限制。建议对敏感系统进行完全封锁。
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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.