MULTI-VECTOR DISTRIBUTED DENIAL-OF-SERVICE MITIGATION USING ZERO-TRUST IDENTITY AND PREDICTIVE ARTIFICIAL INTELLIGENCE-BASED DISTRIBUTED DENIAL-OF-SERVICE OPEN THREAT SIGNALING

Inventor(s)

• Siva Kondala Rao Mandalapu
• Vivek Sinha
• Ram Mohan R

Abstract

Proposed herein is an integrated, intelligence-driven architecture and system for detecting and mitigating multi-vector distributed denial-of-service (DDoS) attacks by unifying telemetry, zero-trust identity signals, machine-learning prediction, and DDoS Open Threat Signaling (DOTS)-based inter-domain coordination. Unlike conventional solutions that analyze traffic in isolation, the proposed architecture correlates identity context with multi-layer traffic behavior and adapts through a federated learning feedback loop to mitigate DDoS threats or attacks. The resulting approach provides a proactive, accurate, and collaboratively orchestrated defense that can respond to evolving DDoS threats in real time.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Mandalapu, Siva Kondala Rao; Sinha, Vivek; and Mohan R, Ram, "MULTI-VECTOR DISTRIBUTED DENIAL-OF-SERVICE MITIGATION USING ZERO-TRUST IDENTITY AND PREDICTIVE ARTIFICIAL INTELLIGENCE-BASED DISTRIBUTED DENIAL-OF-SERVICE OPEN THREAT SIGNALING", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10492