Redefining Defense Thresholds: The Essence of T-Level Traffic Attacks

1. Instant Bandwidth Overload: Single-node bandwidth limitations cause cleaning failures—e.g., a 1Tbps attack overwhelms a 200Gbps node, bypassing defenses directly.
2. Protocol-Level Deception: Hybrid attacks (e.g., SYN Flood + HTTP POST Flood) mimic legitimate traffic, evading traditional rule engines.
3. Sustained Resource Drain: 38% of attacks last over 4 hours, testing long-term system stability.
    

5 Core Technical Metrics for T-Level Defense (No Marketing Jargon—Pure Tech Analysis)

1. Distributed Cleaning Cluster Scale (Critical Hardware Metric)

• Total Cleaning Bandwidth: Require 10T+ distributed cleaning capacity, with single-node processing ≥1.5Tbps (e.g., a Hong Kong node resisted 1.8Tbps UDP reflection attacks in testing). Differentiate "peak bandwidth" from "sustained cleaning bandwidth"—some vendors claim 5T defense but only sustain 5 minutes.
• Node Density & Distribution: >20 nodes per continent (e.g., 15 nodes across Hong Kong, Singapore, Tokyo in APAC) for localized traffic cleaning, minimizing cross-continent latency (ideal origin return latency <50ms).

2. Intelligent Scheduling Architecture

• Anycast Technology: Maps origin IPs to global nodes via BGP Anycast, forcing attackers to nearest nodes (e.g., Beijing users resolve to Tianjin), dispersing traffic across 30+ nodes and reducing single-node load by 70%.
• Dynamic Routing Algorithms: Real-time link quality monitoring (auto-switching at >5% packet loss). Case: A live streaming platform with BGP-enabled CDN switched nodes in <80ms during a 2.3Tbps attack, undetectable to users.

3. AI-Driven Detection (Application-Layer Protection Core)

• Behavioral Baseline Modeling: 7+ days of traffic data train 18-dimensional dynamic models (geolocation, request intervals, device fingerprints), triggering secondary checks for 30% baseline deviations (false positive rate <0.01%).
• Deep Protocol Parsing: Detects attacks on HTTP/3, QUIC, etc.—e.g., identifying QUIC reflection attacks with fake source IPs, impossible for legacy L4 defenses.

4. Elastic Scaling Mechanisms (For Sudden Peaks)

• Hot-Standby Cluster Loading: 30% pre-allocated redundant bandwidth (e.g., 5T daily + 3T standby), enabling sub-minute scaling via hardware acceleration (a financial client scaled from 8T to 15T in 12 minutes).
• Traffic Throttling Strategy: Auto-schedules overload (>80% node usage) to adjacent nodes, returning 101 Switching Protocols to guide users to backups.
   

5. Hardware Acceleration & Protocol Optimization

• NPU Chip Deployment: Edge nodes with Huawei Atlas 500 process 100Gbps line-rate, achieving <500μs hardware ACL matching—3x faster than software-only solutions.
• TCP/IP Stack Hardening: Implements RFC3326 source port filtering and RFC2827 ingress filtering to block IP spoofing at the protocol layer—critical for reflection attack defense.

5-Step Selection Framework: From Requirements to Testing

Step 1: Quantify Defense Needs (Avoid Over/Under-Protection)

• Historical Traffic Analysis: Use Wireshark to calculate 3-month 95th percentile peaks (e.g., 800Gbps peak during e-commerce promotions requires 1.2Tbps buffer, 1.5x redundancy).
• Attack Simulation: Test bottlenecks with LOIC/HULK (e.g., 1Tbps UDP Flood causing server connection exhaustion or >20% CDN packet loss).

Step 2: Validate Cleaning Capability (No Theories—Real Data)

• Node Efficiency Testing: Request real attack logs (timestamps, traffic curves, interception rate). Key metrics:

  • 99.5% interception rate during >1Tbps attacks

  • <0.1% abnormal packets in cleaned origin traffic (verified via tcpdump)
• Multi-Node Load Balancing: Use MTR to monitor scheduling—ideal single-node load <70% of peak (e.g., 2T node handles <1.4T).

Step 3: Assess Application-Layer Protection (T-Level "Soft Barriers")

• CC Attack Mitigation Test: Simulate 500k QPS HTTP GET Flood with JMeter—ensure malicious IP detection (e.g., CAPTCHA for >200 requests/min per IP) and >98% legitimate user pass-through.
• Protocol Compliance Check: Validate SM2/SM3/SM4 (China’s cryptographic standards) and TLS 1.3 support—critical for finance/government (case: a vendor failed healthcare compliance due to SM4 absence).
   

Step 4: Test Elastic Scaling & Response (Last Line of Defense)

• Standby Cluster Switching: Trigger overload (1.5T on single node), measure activation time (<2 minutes ideal) and user continuity (monitored via Selenium).
• Support Responsiveness: Test 2AM emergency tickets—ensure custom policies (e.g., URI rate limiting) delivered within 15 minutes for APT attacks.

Step 5: Long-Term Stability & Costing (Avoid "Unaffordable Defense")

• Billing Model Analysis: Prefer "95th percentile + traffic package" (e.g., 500Gbps daily, 1.2T peak billing saves 37% vs. fixed bandwidth).
• Node Health Monitoring: Require SLA (>99.99% availability) and real-time API access (Prometheus for load/packet loss metrics).
   

Case Study: Building a T-Level Defense for a Gaming Company

1. Challenges: Legacy CDN failed at 1.5T, causing 5s login delay and 40% disconnection; open API CC attacks exhausted database connections.
2. Solutions:
   • Hardware: NPU-equipped CDN07 nodes (1.8T single-node capacity, 32 global nodes).
   • Strategy: AI-driven session fingerprinting with unique user tokens, limiting 100 connections/sec per IP.
   • Emergency: 3T standby bandwidth, auto-dispersing traffic across 12 nodes via Anycast in 1 minute.
3. Results:
   • <200ms login delay, <5% disconnection during attacks.
   • Malicious packets in origin traffic dropped from 35% to 0.08%, database load reduced 65%.
      

6 Pitfalls in T-Level CDN Selection (and How to Avoid Them)

Conclusion: T-Level Defense = Technology + Expertise

1. Data Rules: Insist on Measured logs and stress test results over marketing claims.
2. Context Matters: Gaming needs TCP connection protection; finance requires State secret encryption—no one-size-fits-all.
3. Practice Makes Perfect: Quarterly T-level attack simulations to validate node switching and strategy tuning.