
ABUSE.MOM — 规矩点,否则你将被曝光
| 签名 | 描述 | 分数 | 严重性 |
|---|---|---|---|
| Directory Scan | 自动分析检测到行为异常 | +0 |
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.
将70.179.29.73添加到防火墙封锁列表。检查日志中的成功连接。在所有面向公众的服务上启用全面日志记录。
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。
70.179.29.73 has been assigned a threat score of 60/100 (High). 该IP被评为高级别威胁。网络管理员应实施阻止规则并监控来自此地址的任何连接。
地址70.179.29.73来源于San Diego, United States,运营在Cox Communications 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17天的时间内,此IP产生了281次恶意请求,平均每天约16.5次请求。 从住宅网络运营,此IP可能代表一个被入侵的家庭网关或已被招募到更大攻击基础设施中的IoT设备。 我们的记录显示来自United States的36,339个恶意IP,使其成为全球威胁活动的主要贡献者。 评分60/100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.
CDNs can inadvertently mask the true origin of malicious traffic, making attribution difficult. Attackers abuse CDN services to proxy their attacks, leverage cached content for amplification, and exploit misconfigurations in CDN-to-origin connections.
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.