NEUROGAURD: Adaptive Cognitive Threat Mitigation System

This project introduces NeuroGuard, a flexible approach to managing digital risks through immediate identification and handling of online dangers. While standard tools perform well when confronting familiar attacks, they fall short with new, previously unseen strategies. Detection methods relying on algorithms learn from data yet frequently raise incorrect alerts, grow inefficient at scale, or miss surrounding conditions. Built to overcome such issues, the solution uses separate components working together - one applies simple rules quickly; another employs brain-inspired models that spot irregular actions within data flows. Threats identified during monitoring appear across a framework of consistent attack patterns, enabling clear understanding through organized data. From there, responses shift automatically - malicious IP addresses get denied, affected devices are separated from networks, notifications move toward human reviewers - all helpinto orten reaction periods. Running on Python, the core uses neural models to study behavior, tools that dissect data flow, alongside databases built for storing and reviewing event records. Testing unfolds with recognized cybersecurity datasets, mixed into real-time stream simulations, measuring how fast and accurately risks emerge under varying loads.