终端拒绝服务攻击通过消耗目标系统资源或触发致命错误导致服务不可用，传统防御主要依赖流量基线分析、协议异常检测和资源监控。典型缓解措施包括部署网络流量清洗设备、实施协议合规性检查、配置资源使用阈值告警等。防御方通过监控TCP连接速率、异常协议行为、CPU/内存使用率等指标识别攻击活动。
为规避传统检测机制，攻击者发展出多维匿迹技术，通过协议层深度伪装、加密信道滥用、资源消耗模式优化及漏洞精准利用等策略，将DoS攻击解构为具有合法外衣的隐蔽操作，在维持攻击效能的同时大幅降低行为可观测性。
当前终端DoS匿迹技术的核心演进方向体现为攻击载体的环境融合与破坏机制的不可逆性设计。分布式低频攻击通过僵尸网络的时空分散性，将资源消耗过程融入系统正常负载波动范围；协议模拟反射攻击利用标准协议规范的灰色地带，构造兼具协议合规性与系统破坏性的双重特性载荷；加密洪水攻击则建立加密信道的内容保护屏障，使深度检测技术失去分析锚点；零日漏洞攻击通过精准利用未公开缺陷，实现"四两拨千斤"的隐蔽破坏效果。这些技术的共性在于突破传统流量层对抗模式，通过协议栈深度操作、加密技术滥用、系统脆弱点精准打击等手法，将攻击行为嵌入目标环境的正常交互范式中。
匿迹技术的演进导致传统阈值检测和特征匹配机制逐步失效，防御方需构建加密流量行为分析、协议状态机合规验证、资源消耗模式学习等新型能力，同时建立漏洞情报共享机制和弹性架构设计，实现对隐蔽DoS攻击的多维度纵深防御。
| 效应类型 | 是否存在 |
|---|---|
| 特征伪装 | ✅ |
| 行为透明 | ✅ |
| 数据遮蔽 | ✅ |
| 时空释痕 | ✅ |
攻击者通过精确模拟合法协议交互（如完整实现HTTP/2协议栈）、构造符合加密规范的数据流，使DoS流量在协议特征层面与正常业务流量无法区分。例如在加密洪水攻击中，外层TLS握手过程完全合规，内层恶意载荷被加密保护，实现攻击流量的深度伪装。
零日漏洞攻击利用未知系统缺陷直接触发服务崩溃，由于防御方缺乏漏洞特征认知，攻击过程无法被现有检测规则识别。此类攻击在目标系统日志中仅表现为意外终止，缺乏可追溯的异常行为证据。
加密载荷洪水攻击全程使用TLS/SSL加密，攻击者注入的高复杂度计算请求被加密保护，防御方无法通过流量解密以外的途径获取攻击载荷特征，形成数据层面的完全遮蔽。
分布式低频攻击通过全球僵尸网络实施长时间、低强度的资源消耗，单个节点的请求速率始终低于检测阈值，攻击特征被稀释在正常业务流量中。节点IP的动态轮换和地理分布特性进一步破坏攻击行为的时空关联性。
| ID | Name | Description |
|---|---|---|
| S0052 | OnionDuke |
OnionDuke has the capability to use a Denial of Service module.[1] |
| G0034 | Sandworm Team |
Sandworm Team temporarily disrupted service to Georgian government, non-government, and private sector websites after compromising a Georgian web hosting provider in 2019.[2] |
| S0412 | ZxShell |
ZxShell has a feature to perform SYN flood attack on a host.[3][4] |
| ID | Mitigation | Description |
|---|---|---|
| M1037 | Filter Network Traffic |
Leverage services provided by Content Delivery Networks (CDN) or providers specializing in DoS mitigations to filter traffic upstream from services.[5] Filter boundary traffic by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport. To defend against SYN floods, enable SYN Cookies. |
| ID | Data Source | Data Component | Detects |
|---|---|---|---|
| DS0015 | Application Log | Application Log Content |
Monitor for third-party application logging, messaging, and/or other artifacts that may perform Endpoint Denial of Service (DoS) attacks to degrade or block the availability of services to users. In addition to network level detections, endpoint logging and instrumentation can be useful for detection. Attacks targeting web applications may generate logs in the web server, application server, and/or database server that can be used to identify the type of attack, possibly before the impact is felt. Externally monitor the availability of services that may be targeted by an Endpoint DoS. |
| DS0029 | Network Traffic | Network Traffic Content |
Monitor and analyze traffic patterns and packet inspection associated to protocol(s) that do not follow the expected protocol standards and traffic flows (e.g extraneous packets that do not belong to established flows, gratuitous or anomalous traffic patterns, anomalous syntax, or structure). Consider correlation with process monitoring and command line to detect anomalous processes execution and command line arguments associated to traffic patterns (e.g. monitor anomalies in use of files that do not normally initiate connections for respective protocol(s)). |
| Network Traffic Flow |
Monitor network data for uncommon data flows. Processes utilizing the network that do not normally have network communication or have never been seen before are suspicious. |
||
| DS0013 | Sensor Health | Host Status |
Detection of Endpoint DoS can sometimes be achieved before the effect is sufficient to cause significant impact to the availability of the service, but such response time typically requires very aggressive monitoring and responsiveness. Monitor for logging, messaging, and other artifacts highlighting the health of host sensors (ex: metrics, errors, and/or exceptions from logging applications) |