Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning最新文献

Poisoning Attack Anticipation in Mobile Crowdsensing: A Competitive Learning-Based Study 移动群体感知中的中毒攻击预测:一个基于竞争学习的研究

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469050

Alexandre Prud'Homme, B. Kantarci

引用次数: 3

Multi-Agent Reinforcement Learning Approaches to RF Fingerprint Enhancement 射频指纹增强的多智能体强化学习方法

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469037

Joseph M. Carmack, Steve Schmidt, Scott Kuzdeba

引用次数: 3

Adversarial Learning for Cross Layer Security 跨层安全的对抗性学习

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469043

Hesham Mohammed, D. Saha

引用次数: 0

RiftNeXt™

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469045

Steve Schmidt, J. Stankowicz, Joseph M. Carmack, Scott Kuzdeba

{"title":"RiftNeXt™","authors":"Steve Schmidt, J. Stankowicz, Joseph M. Carmack, Scott Kuzdeba","doi":"10.1145/3468218.3469045","DOIUrl":"https://doi.org/10.1145/3468218.3469045","url":null,"abstract":"We propose a framework, RiftNeXtTM, to perform radio frequency (RF) scene context change detection and classification with Expert driven neural explainability. Our approach uses a deep learning based classifier to perform spectrum monitoring of Wi-Fi devices and usage patterns with an auxiliary classifier operating post-hoc to output human interpretable reasoning for classification declarations. The classification network operates on input spectrograms through a series of dilated causal convolution layers for feature extraction which are fed into classification layers. We have previously shown that dilated causal convolutions are well suited for RF applications, including RF fingerprinting, and extend their use here to new applications. The Explainability Module operates over an auxiliary dataset that is built based on domain expertise for learning how to reason over the classification network outputs. These two approaches, the deep learning classifier and Explainability Module are combined into a unique explainable deep learning approach that we apply to Wi-Fi spectrum monitoring. This paper provides results from this fused approach, leveraging the power of deep learning classification with user interpretable explainability.","PeriodicalId":318719,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122759311","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: 4

Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning-driven Detection Strategy 卫星遥测与遥控系统的间歇性干扰与学习驱动的检测策略

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469041

Selen Gecgel, Günes Karabulut-Kurt

引用次数: 7

Adversarial Attacks on Deep Learning-based Floor Classification and Indoor Localization 基于深度学习的地板分类和室内定位的对抗性攻击

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469052

Mohini Patil, Xuyu Wang, Xiangyu Wang, S. Mao

引用次数: 9

SWIPEGAN SWIPEGAN

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469039

Attaullah Buriro, F. Ricci, B. Crispo

{"title":"SWIPEGAN","authors":"Attaullah Buriro, F. Ricci, B. Crispo","doi":"10.1145/3468218.3469039","DOIUrl":"https://doi.org/10.1145/3468218.3469039","url":null,"abstract":"Behavioral biometric-based smartphone user authentication schemes based on touch/swipe have shown to provide the desired usability. However, their accuracy is not yet considered up to the mark. This is primarily due to the lack of a sufficient number of training samples, e.g., swiping gestures1: users are reluctant to provide many. Consequently, the application of such authentication techniques in the real world is still limited. To overcome the shortage of training samples and make behavioral biometric-based schemes more accurate, we propose the usage of Generative Adversarial Networks (GAN) for generating synthetic samples, in our case, or swiping gestures. GAN is an unsupervised approach for synthetic data generation and has already been used in a wide range of applications, such as image and video generation. However, their use in behavioral biometric-based user authentication schemes has not been explored yet. In this paper, we propose SWIPEGAN - to generate swiping samples to be used for smartphone user authentication. Extensive experimentation and evaluation show the quality of the generated synthetic swiping samples and their efficacy in increasing the accuracy of the authentication scheme.","PeriodicalId":318719,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127812410","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: 7

Efficient and Privacy-preserving Distributed Learning in Cloud-Edge Computing Systems 云边缘计算系统中高效且保护隐私的分布式学习

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469044

Yili Jiang, Kuan Zhang, Y. Qian, R. Hu

{"title":"Efficient and Privacy-preserving Distributed Learning in Cloud-Edge Computing Systems","authors":"Yili Jiang, Kuan Zhang, Y. Qian, R. Hu","doi":"10.1145/3468218.3469044","DOIUrl":"https://doi.org/10.1145/3468218.3469044","url":null,"abstract":"Machine learning and cloud computing have been integrated in diverse applications to provide intelligent services. With powerful computational ability, the cloud server can execute machine learning algorithm efficiently. However, since accurate machine learning highly depends on training the model with sufficient data. Transmitting massive raw data from distributed devices to the cloud leads to heavy communication overhead and privacy leakage. Distributed learning is a promising technique to reduce data transmission by allowing the distributed devices to participant in model training locally. Thus a global learning task can be performed in a distributed way. Although it avoids to disclose the participants' raw data to the cloud directly, the cloud can infer partial private information by analyzing their local models. To tackle this challenge, the state-of-the-art solutions mainly rely on encryption and differential privacy. In this paper, we propose to implement the distributed learning in a three-layer cloud-edge computing system. By applying the mini-batch gradient decent, we can decompose a learning task to distributed edge nodes and participants hierarchically. To improve the communication efficiency while preserving privacy, we employ secure aggregation protocol in small groups by utilizing the social network of participants. Simulation results are presented to show the effectiveness of our proposed scheme in terms of learning accuracy and efficiency.","PeriodicalId":318719,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127188012","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: 0

Machine Learning-Assisted Wireless PHY Key Generation with Reconfigurable Intelligent Surfaces 具有可重构智能表面的机器学习辅助无线PHY密钥生成

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469042

Long Jiao, Guohua Sun, Junqing Le, K. Zeng

引用次数: 9

A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs 基于ofdm的无人机干扰检测与分类的机器学习方法

Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning Pub Date : 2021-06-28 DOI: 10.1145/3468218.3469049

Jered Pawlak, Yuchen Li, Joshua Price, M. Wright, K. Shamaileh, Quamar Niyaz, V. Devabhaktuni

{"title":"A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs","authors":"Jered Pawlak, Yuchen Li, Joshua Price, M. Wright, K. Shamaileh, Quamar Niyaz, V. Devabhaktuni","doi":"10.1145/3468218.3469049","DOIUrl":"https://doi.org/10.1145/3468218.3469049","url":null,"abstract":"In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks on unmanned aerial vehicles (UAVs). Four attack types are implemented using software-defined radio (SDR); namely, barrage, single-tone, successive-pulse, and protocol-aware jamming. Each type is launched against a drone that uses orthogonal frequency division multiplexing (OFDM) communication to qualitatively analyze its impacts considering jamming range, complexity, and severity. Then, an SDR is utilized in proximity to the drone and in systematic testing scenarios to record the radiometric parameters before and after each attack is launched. Signal-to-noise ratio (SNR), energy threshold, and several OFDM parameters are exploited as features and fed to six ML algorithms to explore and enable autonomous jamming detection/classification. The algorithms are quantitatively evaluated with metrics including detection and false alarm rates to evaluate the received signals and facilitate efficient decision-making for improved reception integrity and reliability. The resulting ML approach detects and classifies jamming with an accuracy of 92.2% and a false-alarm rate of 1.35%.","PeriodicalId":318719,"journal":{"name":"Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning","volume":"64 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114016921","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: 8