The traffic cleaning technology of defensive servers such as the US high defense server does not simply intercept or discard data, but uses a multi-layer mechanism to achieve millisecond level "sorting" when attack traffic and legitimate requests are highly mixed to ensure that services can continue to run when attacks occur.

Attack traffic is disguised and infiltrated, and the protocol mimics requests using standard HTTP/HTTPS protocols to bypass primary filtering rules based on ports or protocols. The behavior simulates the click frequency and access path of real users, and even carries legitimate Cookie information. IP forgery uses botnets to rotate source IP addresses to disable IP blacklist-based defense. Because the traditional rate-limiting policy cannot distinguish the attack traffic from the real kill traffic, a large number of normal users are mistakenly killed.

The cleaning system will generally provide four filters, the first layer of flow traction and shunt. When detecting that the attack traffic exceeds the threshold, the cleaning system uses BGP (Border Gateway Protocol) or DNS to redirect the traffic destined for the target server to the distributed cleaning node. This process is similar to the temporary opening of a drainage channel during a flood - globally located cleaning nodes (e.g., Europe, Asia, the Americas) can receive traffic nearby, avoiding delays in transcontinental transmission. A financial platform connected to the cleaning network was able to switch attack traffic from the Tokyo node to the Frankfurt node in 15 seconds, increasing latency for Asian users by only 20ms.

Us high security server second layer feature recognition and coarse screening. Protocol compliance check: Discards malformed packets (such as fragmented SYN packets and excessively long HTTP headers) that clearly violate RFC standards. The IP reputation database matches and compares known malicious IP libraries (such as Spamhaus) to intercept requests initiated by historical attackers. Rate baseline alignment, based on historical traffic models, instantaneously blocks IP or sessions outside the normal fluctuation range (e.g., 3 standard deviations). This phase can filter about 40%-60% of low-level attack traffic, but has limited effect on advanced camouflage attacks.

The third layer behavior fingerprint in-depth analysis. This is where the real technological battle begins. The cleaning system constructs the "digital fingerprint" of the request by calculating the behavioral characteristics of hundreds of dimensions in real time. The time series is abnormal: there are random intervals between normal user visits, and the attack traffic often presents a mechanical precision rhythm; Mouse movement trajectory, through JavaScript injection, detect whether the mouse movement coordinates of the front-end operation conform to the human operation mode; SSL handshake characteristics, analyzing details such as cipher suite selection order, extended fields, etc. during TLS handshakes - Connections generated by automated tools often expose fixed patterns.

The fourth layer challenges verification and dynamic release, for the traffic that cannot be clearly determined, the system initiates interactive verification: static verification code, traditional but effective, can intercept most primary automation tools; The silent challenge returns specific JavaScript code that requires the client to perform a calculation and then submit the result. Legitimate browsers can do this automatically, while most attack tools cannot parse; Device fingerprint verification collects client screen resolution, font list, WebGL rendering features, etc., to build device unique identification. Such verification can increase the cost of an attack by more than 10 times. After a game company added dynamic challenges, the number of attacks against the login interface dropped by 83%.

The pure traffic after cleaning must be safely returned to the source US high security server. For services that need to remain connected, such as online payments, the cleaning center reconstructs the context of the TCP session to ensure that transactions are not interrupted. Replace the source IP address of the injection traffic with the IP address of the cleaning node to prevent attackers from obtaining the real server address. Control the reinjection rate to prevent the normal flow peak from flooding the source station.

The technical difficulty is maintaining business continuity. The defense requires 10 times the bandwidth and computing resources of the attack to maintain the cleaning performance. A 2Tbps attack can cost a cleaning center over a million dollars a day. Attackers are starting to use Generative Adversarial networks (Gans) to simulate human operational behavior, forcing defenders to introduce more complex behavioral models. The popularity of HTTPS makes it impossible to detect traffic content directly, and defenders can only analyze traffic content through metadata (such as packet length sequence and timing characteristics), and the accuracy rate is reduced by about 30%.