Sensitivity of Dynamic Network Slicing to Deep Reinforcement Learning Based Jamming Attacks
Sensitivity of Dynamic Network Slicing to Deep Reinforcement Learning Based Jamming Attacks
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DOI：
10.1109/pimrc56721.2023.10293797
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Feng Wang;M. C. Gursoy;Senem Velipasalar
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文献类型：
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作者：
Feng Wang;M. C. Gursoy;Senem Velipasalar
在本文中，我们考虑在具有多个基站和多个用户的动态环境中基于多代理深度强化学习（deep RL）的网络切片代理。我们开发了一种基于深度RL的干扰机，具有有限的先验信息和有限的功率预算。干扰器的目标是最小化通过网络切片实现的传输速率，从而降低网络切片代理的性能。我们设计了一个具有监听和干扰两个阶段的干扰机，并通过深度RL解决了干扰位置优化和干扰信道优化问题。我们在优化的位置评估干扰机，通过在干扰阶段和监听阶段之间切换，在优化的信道集合中生成干扰攻击。我们表明，所提出的干扰可以显着降低受害者的性能没有直接的反馈或先验知识的网络切片政策。
In this paper, we consider multi-agent deep reinforcement learning (deep RL) based network slicing agents in a dynamic environment with multiple base stations and multiple users. We develop a deep RL based jammer with limited prior information and limited power budget. The goal of the jammer is to minimize the transmission rates achieved with network slicing and thus degrade the network slicing agents’ performance. We design a jammer with both listening and jamming phases and address jamming location optimization as well as jamming channel optimization via deep RL. We evaluate the jammer at the optimized location, generating interference attacks in the optimized set of channels by switching between the jamming phase and listening phase. We show that the proposed jammer can significantly reduce the victims’ performance without direct feedback or prior knowledge on the network slicing policies.