Artificial intelligence-powered intelligent reflecting surface systems - Countering adversarial attacks in machine learning - Defensive distillation technique results

Aug 5, 2026

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AI-powered intelligent reflecting surface (IRS) systems are investigated for next-generation wireless networks in the presence of adversarial machine learning threats. The study models how an AI-based defense reduces attack susceptibility by applying a defense distillation mitigation approach. Susceptibilities are evaluated under FGSM and BIM adversarial methods to measure robustness and efficiency. Results show defensive distillation improves strength and overall performance by about 22% compared with the AI method under adversarial attack conditions.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Next-generation networks (NextG, 5G, 6G)

IRS-based wireless communication and MIMO

Method

Intelligent reflecting surface wireless communication

Adversarial attacks and susceptibility evaluation (FGSM, BIM)

Defensive distillation mitigation and AI-IRS training

Artificial intelligence-powered intelligent reflecting surface systems countering adversarial attacks in machine learning Rajendiran Muthusamy1, Charulatha Kannan2, Jayarathna Mani1, Rathinasabapathi Govindharajan3, Karthikeyan Ayyasamy1 1Department of Computer Science and Engineering, Faculty of Computer Science and Engineering, Panimalar Engineering College, Chennai, India 2Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, India 3Department of Mechanical Engineering, Panimalar Engineering College, Chennai, India | Article history:
Received May 6, 2023 Revised Sep 16, 2023 Accepted Sep 27, 2023 | 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.
This is an open access article under the CC BY-SA license.
| | --- | --- | | Keywords:
6G
Adversarial machine learning Intelligent reflecting surfaces Neural network
Next generation networks | | Corresponding Author: Rajendiran Muthusamy Department of Computer Science and Engineering, Faculty of Computer Science and Engineering Panimalar Engineering College Poonamallee, Chennai, Tamil Nadu, India [Email: mrajendiran@panimalar.ac.in](Email: mrajendiran@panimalar.ac.in) Article Info ABSTRACT 1. INTRODUCTION Next generation networks called NextG or 5G and 6G, are gaining more attention in both industry and academia. Consumers are expecting a high demand and new ways of communication. Based as a study by the international telecommunication union, mobile network traffic on 5th or 6th generation future networks will constantly increase year over year using thousands of pentabytes [1], [2] . The principle NextG networksis to transmit data immediately with least amount of delay between hardware and software devices and is commonly used in fields such as e-health medical services,cloning, artificial authenticity, various autonomous vehicles and online e-learning [3] . Next generation technologies are also used to enhance computing and communications systems. Artificial intelligence (AI) is one of the strong platforms that is very important in developing inventory models in the next generation network [4], [5] . An intelligent reflecting system (IRS) upgraded with multiple input and multiple output (MIMO) uses millimeter wavesand is a powerful and efficient method interms of channel capacity and data transmission ratio. It is also capable of reconfiguring wireless systems to obtain more concentration. IRS utilizes a huge amount of minimum-cost passive send-back elements whose signals constructively add to the destination network, improving the output of the wireless communication networks. The AI model reduces its effective training, despite the various tools such as cyber security and AI, yet metamorphic and polymorphic security attacks. These adversarial attacks manipulate the AI model by intentionally mixing the original data with unwanted sign

FAQs

What problem does the document address in AI-IRS systems?

It addresses adversarial machine learning attacks that can manipulate AI models by injecting unwanted signals into training data, increasing vulnerability in next-generation networks.

Which adversarial attack methods are used to evaluate susceptibilities?

The susceptibility of the AI methods is evaluated using FGSM (fast gradient sign method) and BIM (basic iterative method).

How does defense distillation affect performance under adversarial attacks?

The defensive distillation technique increases the strength and performance of the AI method by about 22% under adversarial attack conditions.

Aug 5, 2026

Upload by Miles