UACybeRoss: An Autonomous AI-Driven Security Framework for Neural Threat Detection in Academic IoT Networks

Research Paper: UACybeRoss Framework Autonomous AI-Driven Security for Distributed Edge-Networks Abstract This research introduces UACybeRoss, an independent framework designed for real-time threat detection and autonomous neural network training within distributed IoT infrastructures. The study focuses on mitigating Zero-day exploits by deploying lightweight AI models directly at the network edge. The framework ensures data integrity and proactive defense through localized behavioral analysis and automated response protocols. 1. Introduction The expansion of interconnected IoT devices has significantly increased the attack surface of modern digital infrastructures. Traditional centralized security systems often suffer from latency and high resource consumption. UACybeRoss addresses these limitations by integrating machine learning directly into edge-computing nodes, allowing for decentralized intelligence and immediate threat mitigation. 2. Neural Network Training Methodology The core of UACybeRoss is its specialized AI training pipeline, which operates on the following principles: 2.1 Localized Data Synthesis The framework utilizes localized traffic datasets to train neural networks on specific environment behaviors. This localized approach ensures that the model is fine-tuned to detect anomalies relevant to its specific deployment context. 2.2 Hybrid Deep Learning Architecture UACybeRoss employs a hybrid architecture combining supervised and unsupervised learning. Supervised learning is used for recognizing known attack signatures, while unsupervised learning focuses on anomaly detection to identify previously unknown (Zero-day) threats. 2.3 Model Optimization for Edge Nodes To ensure efficiency on hardware with limited computational power, the framework implements model pruning and quantization. These techniques allow complex neural networks to maintain high detection accuracy while minimizing memory and processing requirements. 3. Cybersecurity and Autonomous Defense UACybeRoss incorporates a proactive defense layer that operates independently of human intervention. 3.1 Automated Threat Hunting The system continuously monitors network packets and system logs for deviations from established behavioral baselines. The AI-driven engine performs real-time inspection to identify malicious intent. 3.2 Dynamic Mitigation Protocols Once a threat is identified, the framework triggers immediate mitigation protocols. This includes the automated isolation of compromised nodes and the dynamic reconfiguration of network policies to prevent lateral movement of the threat. 3.3 Penetration Testing Integration UACybeRoss includes a module for automated vulnerability scanning and verification. By simulating cyber-attacks, the system verifies the robustness of the trained models and identifies potential weaknesses in the network architecture. 4. Open Source and Intellectual Property The project is licensed under the Apache License 2.0. This ensures full transparency of the security logic and provides explicit patent grants to users and contributors. The open-source nature of the framework encourages peer review and collaborative enhancement of the defense algorithms. 5. Conclusion UACybeRoss demonstrates the viability of autonomous AI models in securing distributed networks. By combining localized neural training with proactive cybersecurity defense, the framework provides a scalable and resilient solution for modern digital ecosystems. Project Name: UACybeRoss Field: Artificial Intelligence, Cybersecurity, IoT Security License: Apache License 2.0 Documentation: DOI Registered via Zenodo