Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning最新文献_第2页

Retracted on July 26, 2022: Open set recognition through unsupervised and class-distance learning 2022年7月26日撤销:通过无监督和课堂远程学习开放集识别

Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2020-07-13 DOI: 10.1145/3395352.3402901

Andrew Draganov, Carter Brown, Enrico Mattei, Cass Dalton, Jaspreet Ranjit

引用次数: 3

Adversarial machine learning based partial-model attack in IoT 物联网中基于部分模型攻击的对抗性机器学习

Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2020-06-25 DOI: 10.1145/3395352.3402619

Zhengping Luo, Shangqing Zhao, Zhuo Lu, Y. Sagduyu, Jie Xu

引用次数: 37

Over-the-air membership inference attacks as privacy threats for deep learning-based wireless signal classifiers 无线成员推理攻击对基于深度学习的无线信号分类器的隐私威胁

Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2020-06-25 DOI: 10.1145/3395352.3404070

Yi Shi, Kemal Davaslioglu, Y. Sagduyu

{"title":"Over-the-air membership inference attacks as privacy threats for deep learning-based wireless signal classifiers","authors":"Yi Shi, Kemal Davaslioglu, Y. Sagduyu","doi":"10.1145/3395352.3404070","DOIUrl":"https://doi.org/10.1145/3395352.3404070","url":null,"abstract":"This paper presents how to leak private information from a wireless signal classifier by launching an over-the-air membership inference attack (MIA). As machine learning (ML) algorithms are used to process wireless signals to make decisions such as PHY-layer authentication, the training data characteristics (e.g., device-level information) and the environment conditions (e.g., channel information) under which the data is collected may leak to the ML model. As a privacy threat, the adversary can use this leaked information to exploit vulnerabilities of the ML model following an adversarial ML approach. In this paper, the MIA is launched against a deep learning-based classifier that uses waveform, device, and channel characteristics (power and phase shifts) in the received signals for RF fingerprinting. By observing the spectrum, the adversary builds first a surrogate classifier and then an inference model to determine whether a signal of interest has been used in the training data of the receiver (e.g., a service provider). The signal of interest can then be associated with particular device and channel characteristics to launch subsequent attacks. The probability of attack success is high (more than 88% depending on waveform and channel conditions) in identifying signals of interest (and potentially the device and channel information) used to build a target classifier. These results show that wireless signal classifiers are vulnerable to privacy threats due to the over-the-air information leakage of their ML models.","PeriodicalId":370816,"journal":{"name":"Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132244626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Algorithm selection framework for cyber attack detection 网络攻击检测算法选择框架

Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2020-05-28 DOI: 10.1145/3395352.3402623

Marc Chalé, Nathaniel D. Bastian, J. Weir

引用次数: 5

Investigating a spectral deception loss metric for training machine learning-based evasion attacks 研究用于训练基于机器学习的逃避攻击的频谱欺骗损失度量

Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2020-05-27 DOI: 10.1145/3395352.3402624

Matthew DelVecchio, Vanessa Arndorfer, W. Headley

{"title":"Investigating a spectral deception loss metric for training machine learning-based evasion attacks","authors":"Matthew DelVecchio, Vanessa Arndorfer, W. Headley","doi":"10.1145/3395352.3402624","DOIUrl":"https://doi.org/10.1145/3395352.3402624","url":null,"abstract":"Adversarial evasion attacks have been very successful in causing poor performance in a wide variety of machine learning applications. One such application is radio frequency spectrum sensing. While evasion attacks have proven particularly successful in this area, they have done so at the detriment of the signal's intended purpose. More specifically for real-world applications of interest, the resulting perturbed signal that is transmitted to evade an eavesdropper must not deviate far from the original signal, less the intended information is destroyed. Recent work by the authors and others has demonstrated an attack framework that allows for intelligent balancing between these conflicting goals of evasion and communication. However, while these methodologies consider creating adversarial signals that minimize communications degradation, they have been shown to do so at the expense of the spectral shape of the signal. This opens the adversarial signal up to defenses at the eavesdropper such as filtering, which could render the attack ineffective. To remedy this, this work introduces a new spectral deception loss metric that can be implemented during the training process to force the spectral shape to be more in-line with the original signal. As an initial proof of concept, a variety of methods are presented that provide a starting point for this proposed loss. Through performance analysis, it is shown that these techniques are effective in controlling the shape of the adversarial signal.","PeriodicalId":370816,"journal":{"name":"Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134559866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9