<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Cognitive Radio Networks (CRNs) present a compelling possibility to enable secondary users to take advantage of unused frequency bands in constrained spectrum resources. However, the network is vulnerable to a wide range of jamming attacks, which adversely affect its performance. Several countermeasures proposed in the literature require prior knowledge of the communication network and jamming strategy that are computationally intensive. These solutions may not be suitable for many real-world critical applications of the Internet of Things (IoT). Therefore, a novel self-exploration approach based on deep reinforcement learning is proposed to learn an optimal policy against dynamic attacks in CRN-based IoT applications. This method reduces computational complexity, without prior knowledge of the communication network or jamming strategy. A simulation of the proposed scheme eliminates interference effectively, consumes less power, and has a better Signal-to-Noise Ratio (SNR) than other algorithms. A platform-agnostic and efficient anti-jamming solution is provided to improve CRN’s performance when jamming occurs.</p>
</abstract>

Cognitive Radio Networks (CRNs) present a compelling possibility to enable secondary users to take advantage of unused frequency bands in constrained spectrum resources. However, the network is vulnerable to a wide range of jamming attacks, which adversely affect its performance. Several countermeasures proposed in the literature require prior knowledge of the communication network and jamming strategy that are computationally intensive. These solutions may not be suitable for many real-world critical applications of the Internet of Things (IoT). Therefore, a novel self-exploration approach based on deep reinforcement learning is proposed to learn an optimal policy against dynamic attacks in CRN-based IoT applications. This method reduces computational complexity, without prior knowledge of the communication network or jamming strategy. A simulation of the proposed scheme eliminates interference effectively, consumes less power, and has a better Signal-to-Noise Ratio (SNR) than other algorithms. A platform-agnostic and efficient anti-jamming solution is provided to improve CRN’s performance when jamming occurs.

A Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks

Department of Computer Science and Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Cognitive Radio Networks (CRNs) present a compelling possibility to enable secondary users to take advantage of unused frequency bands in constrained spectrum...

A Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 128 - 146

Eingereicht: 10. Apr. 2023

Akzeptiert: 14. Sept. 2023

DOI: https://doi.org/10.2478/cait-2023-0040

Schlüsselwörter
Cognitive radio network, IoT, Jamming attack, Customized environment, Self exploration, Reinforcement learning

© 2023 Y. Sudha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks

Online veröffentlicht: 30. Nov. 2023

Seitenbereich: 128 - 146

Eingereicht: 10. Apr. 2023

Akzeptiert: 14. Sept. 2023

DOI: https://doi.org/10.2478/cait-2023-0040

SchlüsselwörterCognitive radio network, IoT, Jamming attack, Customized environment, Self exploration, Reinforcement learning

© 2023 Y. Sudha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Cognitive radio network, IoT, Jamming attack, Customized environment, Self exploration, Reinforcement learning