
ABUSE.MOM — 规矩点,否则你将被曝光
| 签名 | 描述 | 分数 | 严重性 |
|---|---|---|---|
| Burst: 22 req / 2s | 请求频率异常——自动扫描 | +35 | |
| Burst: 22 req / 10s | 请求频率异常——自动扫描 | +35 | |
| Foreign referer seen | 来自无关外部域名的Referer | +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.
在nginx中实施limit_req_zone。部署具有DDoS防护的CDN。配置SYN cookies和连接跟踪以限制112.194.88.8。
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 |
|---|---|---|---|
| 21 | FTP | Medium | File Transfer Protocol — often targeted for anonymous login attacks |
| 111 | Unknown | Low | Service on port 111 |
| 9999 | Unknown | Low | Service on port 9999 |
| 10001 | Unknown | Low | Service on port 10001 |
| 10250 | Unknown | Low | Service on port 10250 |
⚠️ 在112.194.88.8上检测到1个高风险端口。 这些服务在没有严格防火墙规则的情况下不应公开访问。
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。
112.194.88.8 has been assigned a threat score of 80/100 (Critical). 凭借此评分,该IP属于严重威胁级别——是我们监控数据库中最危险的地址之一。
The following attack categories were identified:
威胁情报分析将112.194.88.8与来自Chengdu, China,运营在China Unicom CHINA169 Sichuan Province Network的网络中的恶意活动相关联。该地址自首次检测以来一直处于观察状态。 该地址在我们的监控系统中活跃了1天,产生了1次标记请求,速率约为每天1次。 这是一个住宅IP地址,表明可能是被入侵的家用设备,如路由器、智能设备或参与僵尸网络的受感染工作站。 来自此IP的基于速率的攻击旨在通过大量请求洪水压垮服务器资源。 China目前在我们的数据库中占191个被封锁IP,使其成为恶意流量的重要来源。 威胁评分80/100将此IP置于高风险类别。建议在防火墙级别进行封锁。
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.
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.
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.