A practical radio frequency fingerprinting scheme for mobile phones identification

Adversarial attacks and active defense on deep learning based identification of GaN power amplifiers under physical perturbation

With the development of radar technology, the electromagnetic environment becomes more and more complex. The acquisition of labeled data is costly, and the existing radar signal sorting and recognition methods have poor generalization capability. To address this problem, a radar signal recognition technique based on time–frequency analysis and contrastive learning is proposed in this paper. The classification process is improved according to the characteristics of radar signals without using labels. The proposed method, contrastive time–frequency learning, can use the unsupervised data to sort and identify radar signals. By augmenting the data with various channel conditions, the generalization ability of the classification model is also improved, especially for the situation with low SNR. The experiments prove the good performance of the proposed method, and classification accuracy for radar signals can be up to 98.17%. 

Contrastive time-frequency learning for radar signal sorting

With the rapid growth of the global drone market, the security threats caused by drones have attracted widespread attention. Since legitimate and intrusive drones of multiple types may coexist in the sky, distinguishing them becomes critical for security. This paper proposes an ID-based robust individual identification approach toward multi-type non-cooperative drones. Specifically, a novel ID feature and an extraction method are proposed to obtain features from waveforms without the waveform prior knowledge. We leverage deep learning and a data augmentation method to train a robust identification model to distinguish individual drones of the same type. In order to realize the individual identification of different types of drones without increasing the model size, we design a multi-head deep learning model regarding the idea of multi-task learning. The experiment involves 18 drones of three types and their controllers. Results confirm that our approach can achieve excellent performance for multi-type individual drone identification and intrusive drone detection while being robust to noise and channel effects. It is also validated that our approach has good performance for identifying drone controllers which transmit frequency hopping signals. 

An ID-Based Robust Identification Approach Toward Multitype Noncooperative Drones

Radio-frequency fingerprinting identification (RFFI), based on hardware inherent imperfections, is an effective technique for physical authentication in the Internet of Things (IoT). However, due to the nonstationary and open factors in realistic scenes, problems, such as inaccurate fingerprinting extractions and unreliable transmitter identifications, often occur. Specifically, in actual scenarios, there are challenges, such as varying channel status, variable transmission content, insufficient data set acquisition, and the unknown number of rogue devices. In this article, a robust and protocol-agnostic RFFI system is proposed for open-set recognition, which is achieved by a slicing-enhanced preprocessing with noise augmentation and a well-designed bone network with a modified open layer. Moreover, in order to verify the effectiveness and transferability of the designed RFFI system in applications of IoT, comparative experiments are conducted using a real-world data set of automatic-dependent surveillance-broadcast (ADS-B) signals and the open-source FIT/CorteXlab data set. The performance of the designed extractor and classifier, under varying channel and openness conditions, verifies the channel feasibility and openness generalization of our designed system. The results of the ablation experiment indicate that the designed RFFI system can achieve an accuracy of 96.28% in the classification of 50 registered IoT transmitters and 87.52% in open-set recognition of mixed 50 registered and 10 unregistered IoT transmitters. 

Data-Enhancement-Aided Protocol-Agnostic Transmitter Recognition for Open-Set in IoT

Linear frequency modulation (LFM) is a spread spectrum modulation technique that does not require pseudo-random coding sequences, and it is widely used in radar and sonar technology. In many applications, it is required to distinguish the source of the signal. The radio frequency fingerprint (RFF) is one of the most promising methods to identify the signal sources because of its uniqueness. In this paper, a novel method of feature extraction is proposed to identify the RFF of LFM signal. The proposed method can provide a new kind of feature of RFF for the recognition with high accuracy. The feature can be extracted from the whole signal without separating transient signals and steady-state signals, and it independents of the content of the signal. The proposed method can provide the recognition accuracy as high as 93.68%.

Youwei An

Papers

A content-independent method for LFM signal source identification