Abstract is missing.
- Fortifying Your Defenses: Techniques to Thwart Adversarial Attacks and Boost Performance of Machine Learning-Based Intrusion Detection SystemsWenjing Lou. 1 [doi]
- Increasing the Robustness of a Machine Learning-based IoT Malware Detection Method with Adversarial TrainingJózsef Sándor, Roland Nagy, Levente Buttyán. 3-8 [doi]
- How Can the Adversary Effectively Identify Cellular IoT Devices Using LSTM Networks?Zhengping Jay Luo, Will A. Pitera, Shangqing Zhao, Zhuo Lu, Yalin E. Sagduyu. 9-14 [doi]
- Exploring Adversarial Attacks on Learning-based LocalizationFrost Mitchell, Phillip Smith, Aditya Bhaskara, Sneha Kumar Kasera. 15-20 [doi]
- Approximate Wireless Communication for Federated LearningXiang Ma, Haijian Sun, Rose Qingyang Hu, Yi Qian. 21-26 [doi]
- A Key Generation Scheme for IoV Communication Based on Neural Network AutoencodersLiquan Chen, Han Wang, Tianyu Lu, Zeyu Xu. 27-32 [doi]
- Analysis of Lossy Generative Data Compression for Robust Remote Deep InferenceMathew Williams, Silvija Kokalj-Filipovic, Armani Rodriguez. 33-38 [doi]
- Machine Learning-Based Jamming Detection and Classification in Wireless NetworksEnrico Testi, Luca Arcangeloni, Andrea Giorgetti. 39-44 [doi]
- Machine Learning Assisted Physical Layer Secret Key Generation in the One-Time-Pad Encryption SchemeLiquan Chen, Yufan Song, Tianyu Lu, Peng Zhang. 45-50 [doi]
- Hierarchical Over-the-Air Federated Learning with Differential PrivacyZixi Wang, Arick Grootveld, Mustafa Cenk Gursoy. 51-56 [doi]