RUDRAS: A COGNITIVE IMMUNOLOGICAL DEFENSE FIREWALL FOR ZERO-TRUST, AI-NATIVE NETWORK PROTECTION

Abstract

Modern attackers rarely follow obvious paths. Old- style firewalls and tools that only look for known attack patterns often miss new kinds of attacks, quiet intrusions, or abuse by people inside the system especially when this activity is hidden in encrypted traffic or unusual network routes. Instead of relying on those methods alone, Rudras takes a different approach: built in Rust, it combines smart packet filtering, machine-learning methods to spot odd behavior, tight access controls, real-time threat updates, and tamper-resistant logs, all working together in one system. Every device gets its own baseline of “normal” activity and a tailored response plan, so defenses shift as usage evolves rather than staying locked to fixed settings. Over thirty-four safeguards such as deep inspection of messages, checks on web requests, detection of break-in attempts, and monitoring of user actions run in parallel, processed quickly in a single streamlined flow designed for memory safety and low delay. Learning happens locally across machines without sending all raw data to a central hub, yet policies stay consistent thanks to a clear, structured rule model. The paper then explains how Rudras was designed, implemented, and tested in lifelike enterprise conditions, and shows that it fills key gaps left by older security layers while creating room for future work on privacy-preserving sharing of warning signs and collective validation of threats across large networks