Advanced Autonomous AI-Driven Cyber-Security Framework for Isolated LAN Environments

Source summary

Develop an AI-powered security system for high-security air-gapped LANs that operates entirely offline. It must detect anomalous network and process behaviour without external signatures, identify known and unknown malware through behaviour, monitor USB connections in real time and allow unauthorised devices to be blocked, while continuously covering traffic, processes and hardware interactions. The source names autoencoders, random forests and isolation forests as example machine-learning methods rather than an exhaustive architecture.