SENTINEL-AIOps: Autonomous System Telemetry & Heuristic Threat Detection 🛰️

Lead Researcher: Mohosana Maymuna
Affiliation: Zhengzhou University
Project Theme: AiDevOps & Intelligent Infrastructure Resilience

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🔬 Scientific Abstract

Sentinel-AIOps is a research artifact centered on AiDevOps—the integration of artificial intelligence into DevOps workflows to automate system health and security. This prototype evaluates the efficacy of Heuristic Pattern Recognition in distributed environments. By utilizing a decoupled Hybrid Telemetry Engine, the system bridges the gap between low-level hardware metrics and high-level adversarial threat vectors, demonstrating Security Orchestration, Automation, and Response (SOAR) logic in a mobile-first command center.

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🚀 Live Command Center (Visual Evidence)

Fig 1: Sentinel Galactic Ops Interface demonstrating real-time telemetry dispatch and AI-driven remediation protocols.

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📋 Key Technical Features

• Hybrid Telemetry Engine: Real-time hardware extraction from the host kernel via the psutil library.
• Heuristic Threat Matrix (v1.0): Simulated detection of adversarial signatures (SQLi, DDoS, Exfiltration).
• Decoupled REST API Architecture: Professional v1 API gateway connecting a Python-Flask backend to a Flutter client.
• Galactic Glassmorphism UI: A premium "Space-Grade" interface featuring custom StarField painting and real-time pulse animations.

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🛠️ System Architecture

• Backend: Python 3.10+, Flask (REST API Gateway), Flask-CORS.
• Hardware Layer: psutil (Kernel Telemetry Extraction).
• Frontend: Flutter SDK (Dart), HTTP/JSON Telemetry Parsing.

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🏁 Conclusion & Future Horizons

The Sentinel-AIOps framework represents a successful integration of Autonomous Telemetry and Adversarial Modeling. By developing a decoupled, full-stack bridge between a Python-based security kernel and a Flutter-driven Command Center, I have demonstrated the technical feasibility of Real-Time Security Orchestration (SOAR).

This project is a testament to my commitment as a Rank 3rd (out of 41) Software Engineering student to bridge the gap between high-level AI logic and low-level system security.

Future Roadmap:

• Edge Integration: Porting the telemetry engine to IoT edge-nodes for distributed network defense.
• LLM Fine-Tuning: Implementing a localized Llama-3 model for automated "Root Cause Analysis" (RCA).
• Global Impact: Deploying this framework as an open-source tool for NGOs and educational institutions in emerging economies to secure their digital infrastructure.

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"Engineering is not just about building tools; it is about architecting a safer digital future." © 2026 Mohosana Maymuna | Zhengzhou University