Teaser: Data Augmentation with Conditional GAN for Automatic Modulation Classification

Data Augmentation with Conditional GAN for Automatic Modulation Classification
by Mansi Patel, Xuyu Wang, and Shiwen Mao


The full paper will be presented at WiseML 2020, the second ACM Workshop on Wireless Security and Machine Learning, on July 13, 2020.

** Abstract **
As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting diverse applications. Because machine learning has been applied in many IoT systems, the security implications of machine learning need to be studied following an adversarial machine learning approach. In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices. Our numerical results demonstrate the feasibility of this attack to disrupt the decision making in data fusion with limited control of IoT devices, e.g., the attack success rate reaches 83% when the adversary tampers with only 8 out of 20 IoT devices. These results show that the machine learning engine of IoT system is highly vulnerable to attacks even when the adversary manipulates a small portion of IoT devices, and the outcome of these attacks severely disrupts IoT system operations.

** WiseML 2020 **
The second ACM Workshop on Wireless Security and Machine Learning (WiseML 2020) takes place as an online (virtual) conference on July 13, 2020 in conjunction with the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec 2020). The event is hosted by the Institute of Networks and Security at Johannes Kepler University Linz.

The purpose of this workshop is to bring together members of the AI/ML, privacy, security, wireless communications and networking communities from around the world and offer them the opportunity to share the latest research findings in these emerging and critical areas, as well as to exchange ideas and foster research collaborations, in order to further advance the state-of-the-art in security techniques, architectures, and algorithms for AI/ML in wireless communications.

Copyright (c) 2020 Association for Computing Machinery

Impressum: https://wisec2020.ins.jku.at/impressum/