人工智能驱动的智能反射面系统在机器学习中对抗对抗性攻击

Rajendiran Muthusamy, Charulatha Kannan, Jayarathna Mani, Rathinasabapathi Govindharajan, Karthikeyan Ayyasamy
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引用次数: 0

摘要

随着设备计算能力的提高,无线通信开始采用机器学习(ML)技术。智能反射面(IRS)是一种可编程设备,可通过改变其表面的电值和磁值来控制电磁波的传播。最先进的 ML 技术,尤其是基于深度学习(DL)的 IRS 增强通信技术是一个新兴课题。然而,在将 IRS 与其他新兴技术相结合的同时,恶意数据篡改的可能性也很高。下一代网络中的安全威胁、威胁缓解以及人工智能驱动的应用复杂性不断涌现。在这项工作中,我们研究了在下一代网络中增强 IRS 的无线网络防范对抗性机器学习攻击的能力。人工智能(AI)模型利用防御蒸馏缓解技术将攻击的易感性降至最低。结果表明,防御蒸馏技术(DDT)在对抗性攻击下可将人工智能方法的强度和性能提高约 22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence-powered intelligent reflecting surface systems countering adversarial attacks in machine learning
With the increase in the computation power of devices wireless communication has started adopting machine learning (ML) techniques. Intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic wave propagation by changing the electric and magnetic values of its surface. State-of-the-art ML especially on deep learning (DL)-based IRS-enhanced communication is an emerging topic. Yet while integrating IRS with other emerging technologies possibilities of adversarial data creaping is high. Threats to security, their mitigation, and complexes for AI-powered applications in next generation networks are continuously emerging. In this work the ability of an IRS enhanced wireless network in future-generation networks to prevent adversarial machinelearning attacks is studied. The artificial intelligence (AI) model is used to minimize the susceptibility of attacks using defense distillation mitigation technique. The outcome shows that the defensive distillation technique (DDT) increases the strength and performance by around 22% of the AI method under an adversarial attack.
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