Autonomous DDoS Scrubbing at Line Rate

Abstract

The economics of distributed denial-of-service have inverted. Public threat reporting through 2025 records a near-continuous escalation of peak attack volume, from 6.5 Tbps early in the year to 31.4 Tbps in November 2025, the largest yet measured, sustained for roughly 35 seconds. At the same time, 99 per cent of attacks remain under 1 Gbps and most last under a minute. The defensive consequence is unambiguous: an attack now begins and ends faster than a human operator can read the first alert, so mitigation must be autonomous. This paper describes HyperNext Scrub, a sovereign scrubbing service built on four mechanisms. A dual-algorithm filter combines a deterministic volumetric stage with an adaptive machine-learning stage. An autonomous mitigation pipeline detects, classifies, diverts, scrubs, and returns clean traffic in under 60 seconds without a human in the loop. An absorption model carries the everyday band on owned edge capacity and reaches on-demand burst capacity for the rare multi-terabit tail. A line-rate enforcement datapath drops malicious packets at the scrubbing core and re-injects clean traffic to origin. Every packet is inspected inside India. The paper closes with the threat that motivated it: the arrival of frontier AI capable of discovering and weaponising novel attack vectors autonomously, and the defensive posture that answers it.

Contents

1. 011. What changed, and why mitigation must be autonomous
2. 022. The DDoS threat, by the numbers
3. 033. Architecture of the scrubbing centre
4. 044. The dual-algorithm detection and filter mechanism
5. 055. Autonomous mitigation
6. 066. Attack absorption: owned edge and cloud burst
7. 077. The packet destroyer: line-rate enforcement
8. 088. Operations, transparency, and forensic evidence
9. 099. Future scope: AI in the next generation of attacks
10. 1010. Conclusion, references, and author note

Key findings

• The response window has collapsed. The median volumetric flood in 2025 lasted between 35 and 45 seconds and the largest single event on record lasted about 35 seconds, so an attack is over before a human can triage the first alert. On present industry practice the defender operates two orders of magnitude slower than the attacker.
• No single algorithm covers the problem. A fast deterministic stage generates a real-time footprint and holds the volumetric majority at line rate; an adaptive machine-learning stage adjudicates low-and-slow, application-layer, and carpet-bombing traffic, with a false-positive rate held near two hundredths of a per cent. A confidence-weighted fusion step issues a challenge for borderline cases rather than a binary allow or drop.
• Absorption economics decide the design. Each point of presence carries 200 Gbps of owned mitigation capacity, which absorbs almost every attack, and a cloud burst pool of more than 20 Tbps is engaged only above a 180 Gbps divert threshold for the rare multi-terabit tail. Owned capacity for the common case, on-demand capacity for the rare tail.
• The threat that motivates the architecture is the AI-driven DDoS still to come: autonomous vector discovery, adaptive botnets, adversarial evasion, and polymorphic application-layer abuse. The defensive answer is a loop that is itself autonomous, learning continuously, and sovereign, running on Indian soil with traffic and telemetry retained under Indian jurisdiction.