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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝宏昀 | zh_TW |
| dc.contributor.advisor | Hung-Yun Hsieh | en |
| dc.contributor.author | 鄭煥榮 | zh_TW |
| dc.contributor.author | Huan-Jung Cheng | en |
| dc.date.accessioned | 2025-02-25T16:30:23Z | - |
| dc.date.available | 2026-02-11 | - |
| dc.date.copyright | 2025-02-25 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-02-13 | - |
| dc.identifier.citation | [1] D. Zhang, H. Wang, Y. Wang, and J. Ma, Anti-fall: A Non-intrusive and Real-Time Fall Detector Leveraging CSI from Commodity WiFi Devices. Springer International Publishing, 2015, p. 181–193.
[2] H. Sun, L. G. Chia, and S. G. Razul, “Through-Wall Human Sensing With WiFi Passive Radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 4, pp. 2135–2148, 2021. [3] C. Wei, K. Sohn, C. Mellina, A. Yuille, and F. Yang, “ CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning ,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, June 2021, pp. 10 852–10 861. [4] H. Lee, S. Shin, and H. Kim, “ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning,” in Advances in Neural Information Processing Systems, vol. 34. Curran Associates, Inc., 2021, pp. 7082–7094. [5] H.-X. Chen, B.-J. Hu, L.-L. Zheng, and Z.-H. Wei, “An Accurate AoA Estimation Approach for Indoor Localization Using Commodity Wi-Fi Devices,” in 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2018, pp. 1–5. [6] Z. Chen, L. Zhang, C. Jiang, Z. Cao, and W. Cui, “WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM,” IEEE Transactions on Mobile Computing, vol. 18, no. 11, pp. 2714–2724, 2019. [7] A. Zhuravchak, O. Kapshii, and E. Pournaras, “Human Activity Recognition based on Wi-Fi CSI Data – A Deep Neural Network Approach,” Procedia Computer Science, vol. 198, pp. 59–66, 2022. [8] P. Fard Moshiri, R. Shahbazian, M. Nabati, and S. A. Ghorashi, “A CSI-Based Human Activity Recognition Using Deep Learning,” Sensors, vol. 21, no. 21, 2021. [9] J. Ding and Y. Wang, “WiFi CSI-Based Human Activity Recognition Using Deep Recurrent Neural Network,” IEEE Access, vol. 7, pp. 174 257–174 269, 2019. [10] Y. Ren, Z. Wang, Y. Wang, S. Tan, Y. Chen, and J. Yang, “GoPose: 3D Human Pose Estimation Using WiFi,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 6, no. 2, July 2022. [11] S. Li, Z. Liu, Y. Zhang, Q. Lv, X. Niu, L. Wang, and D. Zhang, “WiBorder: Precise Wi-Fi based Boundary Sensing via Through-wall Discrimination,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 4, no. 3, Sept. 2020. [12] Z. Zhang, Z. Hao, X. Dang, and K. Han, “TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device,” Applied Sciences, vol. 13, no. 17, 2023. [13] H. Zhang, Z. Wang, Z. Sun, W. Song, Z. Ren, Z. Yu, and B. Guo, “Understanding the Mechanism of Through-Wall Wireless Sensing: A Model-based Perspective,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 6, no. 4, Jan. 2023. [14] W. Zhang, S. Zhou, D. Peng, L. Yang, F. Li, and H. Yin, “Understanding and Modeling of WiFi Signal-Based Indoor Privacy Protection,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 2000–2010, 2021. [15] S. M. Hernandez and E. Bulut, “Scheduled Spatial Sensing against Adversarial WiFi Sensing,” in 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2023, pp. 91–100. [16] S. Zhou, W. Zhang, D. Peng, Y. Liu, X. Liao, and H. Jiang, “Adversarial WiFi Sensing for Privacy Preservation of Human Behaviors,” IEEE Communications Letters, vol. 24, no. 2, pp. 259–263, 2020. [17] P. Staat, S. Mulzer, S. Roth, V. Moonsamy, M. Heinrichs, R. Kronberger, A. Sezgin, and C. Paar, “IRShield: A Countermeasure Against Adversarial Physical-Layer Wireless Sensing,” in 2022 IEEE Symposium on Security and Privacy (SP), 2022, pp. 1705–1721. [18] Y. Qiao, O. Zhang, W. Zhou, K. Srinivasan, and A. Arora, “PhyCloak: Obfuscating sensing from communication signals,” in 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16). Santa Clara, CA: USENIX Association, Mar. 2016, pp. 685–699. [19] F. Qi, Y. Zhao, M. Z. A. Bhuiyan, T. Hai, M. Islam, S. Zhang, and Z. Tang, “Unauthorized and privacy-intrusive human activity watching through Wi-Fi signals: An emerging cybersecurity threat,” Concurrency and Computation: Practice and Experience, vol. 35, no. 19, p. e7313, 2023. [20] L. Xu, X. Zheng, X. Li, Y. Zhang, L. Liu, and H. Ma, “WiCAM: Imperceptible Adversarial Attack on Deep Learning based WiFi Sensing,” in 2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 2022, pp. 10–18. [21] E. Shalaby, N. ElShennawy, and A. Sarhan, “Utilizing deep learning models in csi-based human activity recognition,” Neural Computing and Applications, vol. 34, no. 8, p. 5993–6010, Jan. 2022. [22] H. Wang, D. Zhang, Y. Wang, J. Ma, Y. Wang, and S. Li, “RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices,” IEEE Transactions on Mobile Computing, vol. 16, no. 2, pp. 511–526, 2017. [23] S. Yousefi, H. Narui, S. Dayal, S. Ermon, and S. Valaee, “A survey on behavior recognition using wifi channel state information,” IEEE Communications Magazine, vol. 55, no. 10, pp. 98–104, 2017. [24] Y. Zhu, Z. Xiao, Y. Chen, Z. Li, M. Liu, B. Y. Zhao, and H. Zheng, “Adversarial wifi sensing,” arXiv preprint arXiv:1810.10109, 2018. [25] ——, “Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors,” in Proceedings 2020 Network and Distributed System Security Symposium, ser. NDSS 2020. Internet Society, 2020. [26] J. E. van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Machine Learning, vol. 109, no. 2, p. 373–440, Nov. 2019. [27] D.-H. Lee, “Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks,” ICML 2013 Workshop : Challenges in Representation Learning (WREPL), 07 2013. [28] X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-Supervised Learning: Generative or Contrastive,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 857–876, 2023. [29] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proceedings of the 37th International Conference on Machine Learning, ser. ICML’20. JMLR.org, 2020. [30] M. Noroozi and P. Favaro, Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. Springer International Publishing, 2016, p. 69–84. [31] S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised Representation Learning by Predicting Image Rotations,” ArXiv, vol. abs/1803.07728, 2018. [32] G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing, “Learning from class-imbalanced data: Review of methods and applications,” Expert Systems with Applications, vol. 73, pp. 220–239, 2017. [33] Z. Chen, J. Duan, L. Kang, and G. Qiu, “Class-Imbalanced Deep Learning via a Class-Balanced Ensemble,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5626–5640, 2022. [34] M. Li, X. Zhang, C. Thrampoulidis, J. Chen, and S. Oymak, “AutoBalance: optimized loss functions for imbalanced data,” in Proceedings of the 35th International Conference on Neural Information Processing Systems, ser. NIPS’21. Red Hook, NY, USA: Curran Associates Inc., 2024. [35] Y. Yang and Z. Xu, “Rethinking the value of labels for improving class-imbalanced learning,” in Proceedings of the 34th International Conference on Neural Information Processing Systems, ser. NIPS ’20. Red Hook, NY, USA: Curran Associates Inc., 2020. [36] J. Kim, Y. Hur, S. Park, E. Yang, S. J. Hwang, and J. Shin, “Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning,” in Proceedings of the 34th International Conference on Neural Information Processing Systems, ser. NIPS ’20. Red Hook, NY, USA: Curran Associates Inc., 2020. [37] Y. Shao, X. Wang, W. Song, S. Ilyas, H. Guo, and W.-S. Chang, “Feasibility of Using Floor Vibration to Detect Human Falls,” International Journal of Environmental Research and Public Health, vol. 18, no. 1, 2021. [38] Y. Li, K. C. Ho, and M. Popescu, “A Microphone Array System for Automatic Fall Detection,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 5, pp. 1291–1301, 2012. [39] W. Li, R. Gao, J. Xiong, J. Zhou, L. Wang, X. Mao, E. Yi, and D. Zhang, “WiFi-CSI Difference Paradigm: Achieving Efficient Doppler Speed Estimation for Passive Tracking,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 8, no. 2, May 2024. [40] H. Li, W. Yang, J. Wang, Y. Xu, and L. Huang, “WiFinger: talk to your smart devices with finger-grained gesture,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ser. UbiComp ’16. Association for Computing Machinery, 2016, p. 250–261. [41] M. Badarna and L. AbedAllah, “Active Down-Sampling Method for Knn When Dealing with Imbalance Dataset,” Advances in Artificial Intelligence and Machine Learning, vol. 04, pp. 2703–2717, 01 2024. [42] J. Hoyos-Osorio, A. Alvarez-Meza, G. Daza-Santacoloma, A. Orozco Gutierrez, and G. Castellanos-Dominguez, “Relevant information undersampling to support imbalanced data classification,” Neurocomputing, vol. 436, pp. 136–146, 2021. [43] A. Agrawal, H. L. Viktor, and E. Paquet, “Scut: Multi-class imbalanced data classification using smote and cluster-based undersampling,” in 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), vol. 01, 2015, pp. 226–234. [44] S.-J. Yen and Y.-S. Lee, “Cluster-based under-sampling approaches for imbalanced data distributions,” Expert Systems with Applications, vol. 36, no.3, Part 1, pp. 5718–5727, 2009. [45] W.-C. Lin, C.-F. Tsai, Y.-H. Hu, and J.-S. Jhang, “Clustering-based undersampling in class-imbalanced data,” Information Sciences, vol. 409-410, pp.17–26, 2017. [46] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE:synthetic minority over-sampling technique,” J. Artif. Int. Res., vol. 16, no. 1, p. 321–357, June 2002. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97018 | - |
| dc.description.abstract | WiFi 通道狀態資訊（CSI）感測技術的持續發展引發了重大的隱私疑慮，因為竊聽者可能透過牆壁監控室內的人類活動。儘管現有研究已證實穿透牆壁的 WiFi 感測技術的可行性，但仍存在一個關鍵限制：大多數研究假設可以取得標記的訓練數據，這在攻擊者無法直接觀察目標活動的對抗性場景中是不切實際的。為了解決這一挑戰，我們提出了 WiSneak 框架，能夠在不需直接觀察目標的情況下實現基於 CSI 的敵對式活動識別。WiSneak 包含兩個關鍵階段：動作標記階段，結合時間模式分析與機器學習模型，從側通道資訊中自動生成訓練標籤；以及動作識別階段，整合自監督預訓練與數據處理，有效處理訓練標籤中的類別不平衡問題。通過全面的實驗，我們的動作標記階段在「躺下」和「站起」動作檢測中分別達到了 0.94 和 0.91 的 F1 分數。在包含五種人類活動的真實世界 CSI 數據集上評估時，我們的動作識別階段展現了優異的性能，在高度不平衡的條件下（少數類別「跑步」和「跌倒」分別只有 5 個和 2 個標記樣本，而多數類別「行走」有 330 個標記樣本），F1 分數達到 0.69，比現有的半監督方法提升了 36.2%。該框架的有效性不僅揭示了 WiFi 感測技術對隱私的潛在威脅，也透過強調現實世界敵對式感測系統的實際限制與能力，為設計更有效的防禦機制提供了寶貴的見解。 | zh_TW |
| dc.description.abstract | The continuous advancement of WiFi Channel State Information (CSI) sensing technology poses significant privacy concerns, as eavesdropper can potentially monitor indoor human activities through walls. While existing research demonstrates the feasibility of through-wall WiFi sensing, a critical limitation remains: most studies assume access to labeled training data, which is unrealistic in adversarial scenarios where attackers cannot directly observe the target's activities. To address this challenge, we propose WiSneak, a novel framework that enables adversarial CSI-based activity recognition without requiring direct observation of the target. WiSneak consists of two key stages: a Motion Labeling Stage that combines temporal pattern analysis with a custom-designed model to automatically generate training labels from side-channel information, and a Motion Recognition Stage that integrates self-supervised pretraining with adaptive data processing to effectively handle class imbalance in the generated labels. Through comprehensive experiments, our Motion Labeling Stage achieves 0.94 and 0.91 F1-scores for "lay down" and "stand up" motion detection respectively. When evaluated on a real-world CSI dataset containing five human activities, our Motion Recognition Stage demonstrates superior performance with a macro F1-score of 0.69, outperforming existing semi-supervised approaches by 36.2% under highly imbalanced conditions where minority classes "run" and "fall" have only 5 and 2 labeled samples respectively, compared to 330 labeled samples for the majority class "walk". The proposed framework's effectiveness not only reveals concerning implications for WiFi sensing privacy but also provides valuable insights for designing more effective defense mechanisms by highlighting the practical constraints and capabilities of real-world adversarial sensing systems. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-25T16:30:23Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-25T16:30:23Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | ABSTRACT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
LIST OF TABLES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi LIST OF FIGURES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii CHAPTER 1 INTRODUCTION. . . . . . . . . . . . . . . . . . . . 1 CHAPTER 2 BACKGROUND AND RELATED WORK. . . . . 3 2.1 Wifi Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.1 Channel State Information Fundamentals . . . . . . . . . . 3 2.1.2 CSI Features in WiFi Sensing . . . . . . . . . . . . . . . . 4 2.1.3 WiFi CSI Activity Recognition using Machine Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Adversarial WiFi Sensing . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 Through-Wall Human Sensing . . . . . . . . . . . . . . . . 6 2.2.2 Privacy Implications and Defense Mechanisms . . . . . . . 7 2.3 Leveraging Unlabeled Data in Machine Learning . . . . . . . . . . 8 2.3.1 Semi-supervised Learning . . . . . . . . . . . . . . . . . . . 8 2.3.2 Self-supervised Learning . . . . . . . . . . . . . . . . . . . 10 2.4 Semi-Supervised Imbalanced Learning . . . . . . . . . . . . . . . . 11 2.4.1 CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning . . . . . . . . . . . . 11 2.4.2 ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-Supervised Learning . . . . . . . . . . . . . . . . . . . 12 2.4.3 Rethinking the Value of Labels for Improving Class-Imbalanced Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4.4 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-Supervised Learning . . . . . . . . . . . . . . . 14 CHAPTER 3 SYSTEM MODEL. . . . . . . . . . . . . . . . . . . . 15 3.1 Problem Background and Assumptions . . . . . . . . . . . . . . . 15 3.1.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . 15 3.1.2 Adversary Model . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Key Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.1 Limited Label Information . . . . . . . . . . . . . . . . . . 17 3.2.2 Class Imbalance . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3.1 Motion Labeling Stage . . . . . . . . . . . . . . . . . . . . 18 3.3.2 Motion Recognition Stage . . . . . . . . . . . . . . . . . . . 19 CHAPTER 4 LABELING UNLABELED DATA USING SIDE CHANNEL INFORMATION. . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2 Exploring Side Channel Information for Motion Labeling . . . . . 23 4.2.1 Time-based Information . . . . . . . . . . . . . . . . . . . . 23 4.2.2 Motion Sequence-Based Information . . . . . . . . . . . . . 24 4.2.3 Floor Vibration and Sound . . . . . . . . . . . . . . . . . . 25 4.2.4 Advanced CSI Features . . . . . . . . . . . . . . . . . . . . 26 4.3 Time-Based Labeling Algorithm . . . . . . . . . . . . . . . . . . . 27 4.3.1 General Approach of the Algorithm . . . . . . . . . . . . . 27 4.3.2 Practical Implementation . . . . . . . . . . . . . . . . . . . 29 4.3.3 Data Acquisition and Preprocessing . . . . . . . . . . . . . 31 4.3.4 Time Point Identification . . . . . . . . . . . . . . . . . . . 33 4.3.5 Machine Learning Models . . . . . . . . . . . . . . . . . . . 35 4.3.6 Custom Loss Function . . . . . . . . . . . . . . . . . . . . . 36 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 CHAPTER 5 AN ENHANCED SEMI-SUPERVISED LEARNING APPROACH FOR IMBALANCED CSI ACTIVITY RECOGNITION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.1 From Limited Labels to Semi-Supervised Learning . . . . . . . . . 39 5.1.1 Labeled Data from Side Channel Information . . . . . . . . 39 5.1.2 Semi-Supervised Learning with Pseudo-Labels . . . . . . . 40 5.1.3 The Challenge of Class Imbalance . . . . . . . . . . . . . . 41 5.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.2.1 Existing Solutions and Their Limitations . . . . . . . . . . 43 5.2.2 Enhanced Modules Overview . . . . . . . . . . . . . . . . . 44 5.3 Data Processing Module . . . . . . . . . . . . . . . . . . . . . . . 45 5.3.1 Downsampling Strategy . . . . . . . . . . . . . . . . . . . . 46 5.3.2 Upsampling Strategy . . . . . . . . . . . . . . . . . . . . . 48 5.4 Model Pretraining Module . . . . . . . . . . . . . . . . . . . . . . 48 5.4.1 Self-Supervised Pretraining . . . . . . . . . . . . . . . . . . 48 CHAPTER 6 PERFORMANCE EVALUATION. . . . . . . . . . 52 6.1 Performance of Time-Based Labeling Algorithm . . . . . . . . . . 52 6.1.1 System Implementation Details . . . . . . . . . . . . . . . . 52 6.1.2 Threshold Values Analysis . . . . . . . . . . . . . . . . . . 52 6.1.3 Window Size Analysis . . . . . . . . . . . . . . . . . . . . . 54 6.1.4 Loss Function Weight Optimization . . . . . . . . . . . . . 57 6.1.5 Performance Comparison with Baseline Methods . . . . . . 60 6.2 Performance of Semi-supervised Learning Approach . . . . . . . . 62 6.2.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . 62 6.2.2 Comparison with Baseline Models . . . . . . . . . . . . . . 65 6.2.3 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . 68 6.2.4 Analysis on Different Label Distribution Scenarios . . . . . 74 6.3 End-to-End Framework Evaluation . . . . . . . . . . . . . . . . . . 81 6.3.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . 81 6.3.2 Results and Analysis . . . . . . . . . . . . . . . . . . . . . 82 CHAPTER 7 CONCLUSION AND FUTURE WORK. . . . . . 84 7.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 | - |
| dc.language.iso | en | - |
| dc.subject | 無標記資料動作辨識 | zh_TW |
| dc.subject | 側通道攻擊隱私 | zh_TW |
| dc.subject | WiFi CSI 感測 | zh_TW |
| dc.subject | Side-channel Privacy Attack | en |
| dc.subject | WiFi Channel State Information Sensing | en |
| dc.subject | Unlabeled Activity Recognition | en |
| dc.title | 類別不平衡下敵對式無線感知模型之標註與學習 | zh_TW |
| dc.title | A Study on Adversarial WiFi Sensing with Class-Imbalanced Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 高榮鴻;沈上翔;葉佳宜 | zh_TW |
| dc.contributor.oralexamcommittee | Rung-Hung Gau;Shan-Hsiang Shen;Chia-Yi Yeh | en |
| dc.subject.keyword | WiFi CSI 感測,無標記資料動作辨識,側通道攻擊隱私, | zh_TW |
| dc.subject.keyword | WiFi Channel State Information Sensing,Unlabeled Activity Recognition,Side-channel Privacy Attack, | en |
| dc.relation.page | 89 | - |
| dc.identifier.doi | 10.6342/NTU202500641 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-02-13 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 電機工程學系 | |
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