Lightweight IDS for UAV Networks: A Periodic Deep Reinforcement Learning-based Approach

Document Type

Conference Proceeding

Source of Publication

Proceedings of the IM 2021 - 2021 IFIP/IEEE International Symposium on Integrated Network Management

Publication Date

5-17-2021

Abstract

The use of intrusion detection systems (IDS) has become crucial for modern networks. To ensure the targeted performance of such networks, diverse techniques were introduced to enhance system reliability. Many network designs have adapted the use of Unmanned Aerial Vehicles (UAVs) to provide wider coverage and meet performance targets. However, the cybersecurity aspect of UAVs has not been fully considered. In this paper, we propose a lightweight intrusion detection and prevention system (IDPS) module for UAVs. The IDPS module is trained using Deep Reinforcement Learning (DRL), specifically Deep Q-learning (DQN), to enable UAVs to autonomously detect suspicious activities and to take necessary action to ensure the security of the network. A customized reward function is used to take into consideration the dataset unbalanced nature, which encourages the IDPS module to detect minor classes. Also, considering the limited availability of resources for UAVs, a periodic offline-learning approach is introduced to ensure that UAVs are capable to learn and adapt to the evolution of intrusion attacks autonomously. Numerical simulations show the efficiency of the proposed IDPS in detecting suspicious activities and corroborating the advantages brought by the periodic offline learning in comparison with similar online learning approaches, in terms of accuracy and energy consumption.

ISBN

9783903176324

Publisher

IEEE

First Page

1032

Last Page

1037

Disciplines

Electrical and Computer Engineering

Keywords

DRL, intrusion detection and prevention system, lightweight models, periodic offline learning, UAVs

Scopus ID

85113595142

Indexed in Scopus

yes

Open Access

no

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