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Multi-Attribute Monitoring for Anomaly Detection: a Reinforcement Learning Approach based on Unsupervised Reward

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Abstract

This paper proposes a new method to solve the monitoring and anomaly detection problems of Low-power Internet of Things (IoT) devices. However, their performances are constrained by limited processing, memory, and communication, usually using battery-powered energy. Polling driven mechanisms for monitoring the security, performance, and quality of service of these networks should be efficient and with low overhead, which makes it particularly challenging. The present work proposes the design of a novel method based on a Deep Reinforcement Learning (DRL) algorithm coupled with an Unsupervised Learning reward technique to build a pooling monitoring of IoT networks. This combination makes the network more secure and optimizes predictions of the DRL agent in adaptive environments.
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Dates and versions

hal-03506409 , version 1 (02-01-2022)

Identifiers

  • HAL Id : hal-03506409 , version 1

Cite

Mohamed Said Frikha, Sonia Mettali Gammar, Abdelkader Lahmadi. Multi-Attribute Monitoring for Anomaly Detection: a Reinforcement Learning Approach based on Unsupervised Reward. PEMWN 2021 - 10th IFIP International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks, Nov 2021, Waterloo, Canada. ⟨hal-03506409⟩
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