IP威胁报告
20.28.242.40
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 |
|---|---|---|---|
| Danger strong hits: 3 | 高风险路径：Webshell、RCE、漏洞利用 | +75 | |
| Danger medium hits: 23 | 中等风险：管理面板、配置文件 | +60 | |
| Burst: 10 req / 2s | 请求频率异常——自动扫描 | +35 | |
| Burst: 25 req / 10s | 请求频率异常——自动扫描 | +35 |
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 20.28.242.40 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20.28.242.40。
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
20.28.242.40 has been assigned a threat score of 205/100 (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
威胁情报分析将20.28.242.40与来自The Rocks, Australia，运营在Microsoft Corporation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。 该IP从数据中心基础设施运营，是有组织攻击行动中使用的典型地址。 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Australia的101个恶意IP，使其成为全球威胁活动的重要贡献者。 评分205/100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 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.
Related Threats
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💡 Vulnerability Scanning Explained
Vulnerability scanning is the automated process of probing web applications for known weaknesses. Attackers use tools like Nuclei, Nikto, and ZAP to test thousands of hosts per hour, looking for exposed configuration files, outdated software, and default credentials.
💡 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.