Algerian Journal of Electrical Systems and Sustainability
Volume 1, Numéro 2, Pages 36-42
2025-12-31
Ai-driven Cyber-attack Detection And Mitigation Algorithm For The Mg-farm Project
Authors : Belmili Hocine .
Abstract
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and renewable-energy micro-grids within the MG-FARM platform offers a transformative approach to enhancing agricultural productivity, energy efficiency, and sustainability in remote regions. While this digital-agricultural convergence enables optimized management of energy, water, and crop systems, it also increases exposure to cyber threats capable of disrupting critical operations such as irrigation scheduling, water pumping, and micro-grid control. Addressing these challenges, this paper introduces a novel AI-driven intrusion detection and mitigation algorithm specifically designed for MG-FARM. The proposed solution combines machine learning-based anomaly detection with resilient control strategies, incorporating real-time sensor data validation, network traffic monitoring, and adaptive response mechanisms. These features ensure service continuity and operational stability even under malicious attack scenarios. Extensive simulations and case studies validate the approach, demonstrating its capacity to detect and neutralize false data injection (FDI) and denial-of-service (DoS) attacks. Results indicate a 32% improvement in system resilience compared to conventional rule-based methods, underscoring the critical role of ontology-aware, AI-enhanced cyber security frameworks in securing next-generation smart farming infrastructures.
Keywords
Smart Farming ; Micro-grids ; Cybers-ecurity ; Intrusion detection system (IDS) ; Artificial Intelligence ; Internet of Things (IoT) ; Agriculture 4.0 ; False Data Injection (FDI) ; Denial-of-Service (DoS)
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