Specific emitter identification (SEI) is extracting the features of the received radio signals and determining the emitter individuals that generate the signals. Although deep learning-based methods have been effectively applied for SEI, their performance declines dramatically with the smaller number of labeled training samples and in the presence of significant noise. To address this issue, we propose an improved Bootstrap Your Own Late (BYOL) self-supervised learning scheme to fully exploit the unlabeled samples, which comprises the pretext task adopting contrastive learning conception and the downstream task. We designed three optimized data augmentation methods for communication signals in the former task to serve the contrastive concept. We built two neural networks, online and target networks, which interact and learn from each other. The proposed scheme demonstrates the generality of handling the small and sufficient sample cases across a wide range from 10 to 400, being labeled in each group. The experiment also shows promising accuracy and robustness where the recognition results increase at 3-8% from 3 to 7 signal-to-noise ratio (SNR). Our scheme can accurately identify the individual emitter in a complicated electromagnetic environment.

Specific Emitter Identification (SEI) is a key research problem in the field of information countermeasures. It is one of the key technologies required to be solved urgently in the target reconnaissance system. It has the ability to distinguish between different individual radiation sources according to the varying individual characteristics of the emitter hardware within the transmitted signals. In response to the lack of scarcity among labeled samples in specific emitter identification, this paper proposes a method combining multi-domain feature fusion and integrated learning (MDFFIL). First, the received signal is preprocessed to obtain segmented time domain signal samples. Then, the signal is converted to time–frequency distribution using wavelet transform. Afterwards, an integrated learning two-stage recognition classification method is designed to extract data features of 1D time domain signals and 2D time–frequency distribution signals using the symmetry network structures of CVResNet and ResNet. Finally, fused features are fed into the complex-valued residual network classifier to obtain the final classification results. We demonstrate through the analysis results of the measured data that the proposed method has a higher accuracy as compared with the classical feature extraction method, and that this can improve the identification of communication radiation sources with fewer labeled samples.

The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS–B (Automatic Dependent Surveillance–Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.

The goal of relational triple extraction is to extract knowledge-rich relational triples from unstructured text. Although the previous methods obtain considerable performance, there are still some problems, such as error propagation, the overlapping triple problem, and suboptimal subject–object alignment. To address the shortcomings above, in this paper, we decompose this task into three subtasks from a fresh perspective: entity extraction, subject–object alignment and relation judgement, as well as propose a novel bi-directional translating decoding model (BTDM). Specifically, a bidirectional translating decoding structure is designed to perform entity extraction and subject–object alignment, which decodes entity pairs from both forward and backward extraction. The bidirectional structure effectively mitigates the error propagation problem and aligns the subject–object pairs. The translating decoding approach handles the overlapping triple problem. Finally, a (entity pair, relation) bipartite graph is designed to achieve practical relationship judgement. Experiments show that our model outperforms previous methods and achieves state-of-the-art performance on NYT and WebNLG. We achieved F1-scores of 92.7% and 93.8% on the two datasets. Meanwhile, in various complementary experiments on complex scenarios, our model demonstrates consistent performance gain in various complex scenarios.

Deep learning models are vulnerable to backdoor attacks. The success rate of textual backdoor attacks based on data poisoning in existing research is as high as 100%. In order to enhance the natural language processing model’s defense against backdoor attacks, we propose a textual backdoor defense method via poisoned sample recognition. Our method consists of two parts: the first step is to add a controlled noise layer after the model embedding layer, and to train a preliminary model with incomplete or no backdoor embedding, which reduces the effectiveness of poisoned samples. Then, we use the model to initially identify the poisoned samples in the training set so as to narrow the search range of the poisoned samples. The second step uses all the training data to train an infection model embedded in the backdoor, which is used to reclassify the samples selected in the first step, and finally identify the poisoned samples. Through detailed experiments, we have proved that our defense method can effectively defend against a variety of backdoor attacks (character-level, word-level and sentence-level backdoor attacks), and the experimental effect is better than the baseline method. For the BERT model trained by the IMDB dataset, this method can even reduce the success rate of word-level backdoor attacks to 0%.

Named entity recognition (NER) aims to extract entities from unstructured text, and a nested structure often exists between entities. However, most previous studies paid more attention to flair named entity recognition while ignoring nested entities. The importance of words in the text should vary for different entity categories. In this paper, we propose a head-to-tail linker for nested NER. The proposed model exploits the extracted entity head as conditional information to locate the corresponding entity tails under different entity categories. This strategy takes part of the symmetric boundary information of the entity as a condition and effectively leverages the information from the text to improve the entity boundary recognition effectiveness. The proposed model considers the variability in the semantic correlation between tokens for different entity heads under different entity categories. To verify the effectiveness of the model, numerous experiments were implemented on three datasets: ACE2004, ACE2005, and GENIA, with F1-scores of 80.5%, 79.3%, and 76.4%, respectively. The experimental results show that our model is the most effective of all the methods used for comparison.