%0 Journal Article
%T Real-Time Cyber Monitoring and Threat Detection System with Hybrid AI Analysis
%A Ashley Audrey Innocent Yanguema
%A Chunyong Yin
%J Open Access Library Journal
%V 13
%N 1
%P 1-16
%@ 2333-9721
%D 2026
%I Open Access Library
%R 10.4236/oalib.1114742
%X Modern Security Operations Centers (SOCs) face the dual challenge of identifying zero-day threats in high-throughput network streams and mitigating analyst alert fatigue. This paper proposes Sentinel AI, a hybrid detection framework orchestrating unsupervised statistical learning with Large Language Model (LLM) reasoning. We introduce a novel dual-engine architecture: a low-latency Isolation Forest model for real-time anomaly filtration ( <strong>O</strong> (<strong>n</strong>) complexity), and a semantic analysis engine utilizing Google Gemini Pro for context-aware threat interpretation and automated playbook execution. We present a reproducible reference architecture based on FastAPI and WebSocket streaming. Experimental validation on synthetic datasets demonstrating DDoS and data exfiltration patterns reveals that Sentinel AI achieves a 93% F1-score, significantly outperforming traditional signature-based baselines in zero-day scenarios, while reducing the cognitive load on analysts through natural language incident reporting.<br />
%K Network Anomaly Detection
%K Large Language Models (LLMs)
%K Hybrid Artificial Intelligence
%K Automated Incident Response
%K Unsupervised Learning
%K Generative AI for Cybersecurity
%K Security Operations Center (SOC)
%U http://www.oalib.com/paper/6883575