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An Improved Anomaly Detection Based on Ensemble Learning and Deep Q-Network for Mobile Edge Computing Monitoring

In: Reliability in Cyber-Physical Systems: The Human Factor Perspective

Author

Listed:
  • Hafida Assmi

    (Cadi Ayyad University, SISAR Team, LaRTiD Lab, Technology Higher School Essaouira)

  • Said Jabbour

    (CRIL—CNRS, Artois University)

  • Azidine Guezzaz

    (Cadi Ayyad University, SISAR Team, LaRTiD Lab, Technology Higher School Essaouira)

  • B. J. Bipin Nair

    (Amrita Vishwa Vidyapeetham, Department of Computer Science, Amrita School of Computing)

  • Said Benkirane

    (Cadi Ayyad University, SISAR Team, LaRTiD Lab, Technology Higher School Essaouira)

  • Mourade Azrour

    (Moulay Ismail University of Meknes, IDMS Team, Faculty of Sciences and Techniques)

Abstract

Mobile edge computing (MEC) improves the performance of real-time services by bringing computing resources closer to users, thus reducing latency and optimizing network utilization. Task offloading plays a key role in this architecture, intelligently distributing loads between edge devices and servers, guaranteeing efficient use of resources and improved energy efficiency. This decentralization exposes edge infrastructures to increased cyber threats, making it essential to integrate intrusion detection systems (IDS) to guarantee the security and reliability of MEC environments. In this paper, we present an IDS model that uses the Deep Q-Network (DQN) algorithm with the ensemble learning method, specifically the voting classifier that combines three algorithms Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting (GB) on the two datasets Shanghai and Guangzhou. The model gave an accuracy between 67 and 99%.

Suggested Citation

  • Hafida Assmi & Said Jabbour & Azidine Guezzaz & B. J. Bipin Nair & Said Benkirane & Mourade Azrour, 2026. "An Improved Anomaly Detection Based on Ensemble Learning and Deep Q-Network for Mobile Edge Computing Monitoring," Springer Series in Reliability Engineering, in: Gururaj H. L. & Vinayakumar Ravi & Hoang Pham & Dayananda P. (ed.), Reliability in Cyber-Physical Systems: The Human Factor Perspective, pages 93-104, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-09917-4_6
    DOI: 10.1007/978-3-032-09917-4_6
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