Visible to the public Biblio

Filters: Author is Lin, Yun  [Clear All Filters]
2021-10-12
Zhao, Haojun, Lin, Yun, Gao, Song, Yu, Shui.  2020.  Evaluating and Improving Adversarial Attacks on DNN-Based Modulation Recognition. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1–5.
The discovery of adversarial examples poses a serious risk to the deep neural networks (DNN). By adding a subtle perturbation that is imperceptible to the human eye, a well-behaved DNN model can be easily fooled and completely change the prediction categories of the input samples. However, research on adversarial attacks in the field of modulation recognition mainly focuses on increasing the prediction error of the classifier, while ignores the importance of decreasing the perceptual invisibility of attack. Aiming at the task of DNNbased modulation recognition, this study designs the Fitting Difference as a metric to measure the perturbed waveforms and proposes a new method: the Nesterov Adam Iterative Method to generate adversarial examples. We show that the proposed algorithm not only exerts excellent white-box attacks but also can initiate attacks on a black-box model. Moreover, our method decreases the waveform perceptual invisibility of attacks to a certain degree, thereby reducing the risk of an attack being detected.
2020-02-17
Lin, Yun, Chang, Jie.  2019.  Improving Wireless Network Security Based On Radio Fingerprinting. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :375–379.
With the rapid development of the popularity of wireless networks, there are also increasing security threats that follow, and wireless network security issues are becoming increasingly important. Radio frequency fingerprints generated by device tolerance in wireless device transmitters have physical characteristics that are difficult to clone, and can be used for identity authentication of wireless devices. In this paper, we propose a radio frequency fingerprint extraction method based on fractional Fourier transform for transient signals. After getting the features of the signal, we use RPCA to reduce the dimension of the features, and then use KNN to classify them. The results show that when the SNR is 20dB, the recognition rate of this method is close to 100%.