IP威胁报告
185.55.4.140
ABUSE.MOM — 规矩点，否则你将被曝光
地理位置与分类
检测签名
| 签名 | 描述 | 分数 | 严重性 |
|---|---|---|---|
| Burst 200/10s | 请求频率异常——自动扫描 | +35 | |
| Burst 200/2s | 请求频率异常——自动扫描 | +35 | |
| Danger medium hits: 12 | 中等风险：管理面板、配置文件 | +60 | |
| Danger strong hits: 65 | 高风险路径：Webshell、RCE、漏洞利用 | +100 | |
| Foreign referer | 来自无关外部域名的Referer | +10 |
观察到的活动
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.
时间线
网络供应商
建议
已采取和建议的措施
- IP 185.55.4.140 已在应用层封锁（HTTP 403）
- 建议在防火墙层（iptables/CSF）进行封锁
- 通过abuse联系方式向网络供应商举报
- 确保敏感文件（.env、.git、备份）无法从网络访问
🌊 洪水/DDoS缓解
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185.55.4.140。
黑名单状态 (DNSBL)
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。
Threat Analysis
185.55.4.140 has been assigned a threat score of 240/100 (Critical). 这代表着极高风险等级。我们的检测系统已从该地址标记出多个高置信度的恶意意图指标。
The following attack categories were identified:
📊 Threat Analysis
地址185.55.4.140来源于Ashburn, United States，运营在Verizon Business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5天的观察窗口期间，我们记录了来自此IP的81次敌对请求——平均每天约16.2次。 该地址作为VPN/代理出口节点运营。攻击者通过匿名化服务路由流量以隐藏真实位置。 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。 我们的记录显示来自United States的36,231个恶意IP，使其成为全球威胁活动的主要贡献者。 评分240/100将此地址置于最高严重性级别。应封锁并调查任何历史连接。
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
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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.