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

Hafida Assmi, Said Jabbour (), Azidine Guezzaz (), B. J. Bipin Nair (), Said Benkirane and Mourade Azrour
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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

A chapter in Reliability in Cyber-Physical Systems: The Human Factor Perspective, 2026, pp 93-104 from Springer

Abstract: 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%.

Keywords: Anomaly detection; Ensemble learning; Deep Q-Network; Mobile edge computing; Monitoring (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-09917-4_6

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